DATA MINING WITH SELF-ORGANIZING MAPS PART I MAIN STEPS

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SOM算法研究与应用

SOM算法研究与应用SOM算法，也称为自组织映射算法（Self-Organizing Maps），是一种无监督学习算法，用于将高维数据映射到低维空间中。

SOM算法由芬兰科学家Teuvo Kohonen于1982年所提出，并且在计算机科学和机器学习领域中具有广泛的应用。

SOM算法的核心思想是通过将输入数据映射到一个拓扑结构上的低维空间中，实现数据的可视化和分类。

SOM网络由一个二维或三维的网格组成，每个网格单元称为节点。

在训练过程中，每个节点与输入数据之间存在权重向量，而权重向量则决定了节点在低维空间中的位置。

SOM算法通过迭代的方式，不断调整权重向量以逼近输入数据的分布特征，从而实现数据的映射和聚类。

1.初始化网络：定义网络的拓扑结构和每个节点的权重向量，通常权重向量随机初始化。

2.选择输入数据：从训练数据集中随机选择一个数据作为当前迭代的输入。

3.计算获胜节点：通过比较输入数据与每个节点的权重向量，选择距离最接近输入数据的节点作为获胜节点。

4.更新获胜节点和邻近节点的权重向量：根据获胜节点和邻近节点的拓扑关系，调整它们的权重向量，使其更接近输入数据。

5.更新学习率和邻域半径：随着迭代的进行，逐渐减小学习率和邻域半径，以缓慢调整节点的权重向量。

6.重复步骤2至5，直到达到指定的迭代次数或网络达到收敛。

1.数据聚类：SOM算法可以将相似的数据映射到相邻的节点上，从而实现聚类。

聚类结果可以帮助我们理解数据的分布特征和相似性，从而进行更深入的分析和决策。

2.数据可视化：SOM算法将高维数据映射到低维空间中，可以将数据可视化为二维或三维的网格结构。

这种可视化方法可以帮助我们直观地理解数据之间的关系和规律。

3.特征提取：SOM算法可以通过调整权重向量的方式，将数据映射到低维空间中，从而实现特征提取。

通过SOM算法提取的特征可以用于后续的分类、聚类或识别任务。

4.异常检测：SOM算法可以识别输入数据与大多数数据不同的节点，从而实现异常检测。

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基于大数据的网络安全信息可视化系统V1.0技术研究报告北京邮电大学目录第一章基于大数据的网络安全信息可视化系统研究背景 (1)1.1 基于大数据的网络安全信息可视化系统研究背景及意义 (1)1.2 基于大数据的网络安全信息可视化技术现状 (3)1.2.1基于网络数据流量的网络安全可视化 (3)1.2.2基于端口信息的网络安全可视化 (3)1.2.3基于入侵检测技术的网络安全可视化 (4)1.2.4基于防火墙事件的网络安全可视化 (4)1.2.5其它 (5)1.3 本章小结 (5)第二章基于大数据的网络安全信息可视化系统概述 (6)2.1 基于大数据的网络安全信息可视化的基本形式 (6)2.2 常用的八种数据可视化方法 (7)2.3 本章小结 (12)第三章基于大数据的网络安全信息可视化系统关键技术 (12)3.1用户接口与体验 (12)3.2图像闭塞性的降低 (14)3.3 端口映射算法 (18)3.4 网络安全态势的评估与入侵分析 (18)3.5 本章小结 (20)第一章基于大数据的网络安全信息可视化系统研究背景1.1 基于大数据的网络安全信息可视化系统研究背景及意义随着网络的普及，互联网上的各种应用得到了飞速发展，而诸多应用对网络安全提出了更高的要求，网络入侵给全球经济造成的损失也在逐年增长。

然而目前网络安全分析人员只能依靠一些网络安全产品来分析大量的日志数据，从而分析和处理异常。

但随着网络数据量的急剧增大，攻击类型和复杂度的提升，这种传统的分析方式已经不再有效。

如何帮助网络安全分析人员通过繁杂高维数据信息快速分析网络状况已经成为网络安全领域一个十分重要且迫切的问题。

网络安全可视化技术就是在这种情况下产生的。

它将海量高维数据以图形图像的方式表现出来，通过在人与数据之间实现图像通信，使人们能观察到网络安全数据中隐含的模式，能快速发现规律并发现潜在的安全威胁。

网络安全可视化的必要性一个安全系统至少应该满足用户系统的保密性、完整性及可用性要求。

时序数据的异常检测可视化综述

时序数据的异常检测可视化综述1介绍时序数据被定义为一系列基于一个准确时间测量的结果，时间间隔通常是规律的[1]。

例如按照一定时间间隔统计到的排名数据，实时检测的传感器数据，社交网络中每天的转发回复数据。

对于时序数据的分析在今天越来越广泛的应用在科学，工程，和商业领域，可视化帮助人们利用感知减少认知负荷进而理解数据[2]。

长期以来，可视化也已经成功的被应用在对于时序数据的分析中来[3]。

例如社交媒体[4]，城市数据[5]，电子交易[6]，时序排名[7]。

在不同领域的时序数据中发现重要的特征和趋势的日益增长的需求刺激了许多可视交互探索工具的发展[8]：Line Graph Explore[9]，LiveRAC[2]，SignalLens[10]和Data Vases[11]等。

时序数据的可视分析任务中，包括特征提取[14]，相关性分析和聚类[7]，模式识别[9]，异常检测[10]等。

而异常检测在不同的研究领域都是一个重要的问题，异常检测表示发现数据中不符合预期行为的模式[12]。

异常检测的目的是找到某些观察结果，它与其他的观察结果有很大的偏差，以至于引起人们怀疑它是由不同的机制产生的[17]。

对应到不同的领域中，网络安全中的异常表示网络设备异常或者可疑的网络状态[13]。

情感分析中的异常表示一组数据中反常的观点，情绪模式，或者产生这些模式的特殊时间[16]。

社交媒体中的异常可以是反常的行为，例如识别网络机器人[20]，反常的传播过程，例如谣言的传播[19]。

这些异常信息或模式的产生原因，可能是会影响日常生活，社会稳定的因素，例如电脑侵入，社交机器人，道路拥堵状况等。

提早发现识别这些异常有助于及时找到产生原因和实际状况，从而进一步分析或解决问题。

异常检测已经有许多成熟的方法，而且在机器学习领域也引起了广泛的关注[12]，包括有监督[21]和无监督的异常检测方法[22]。

自动化的学习算法通常基于这样的假设，即有充足的训练数据可用，同时这些数据理应是正常的行为，否则，正常的学习模型不能把新的观测结果按照异常来进行分类，很有可能新的观测数据是不常见的正常事件[25]，但当涉及到人工标注数据的问题时，往往需要大量的数据，费事费力，难以获取，同时又十分依赖于主观认为的判断，这些极大地影响了最后的分析结果质量[20]。

机器学习与人工智能领域中常用的英语词汇

机器学习与人工智能领域中常用的英语词汇1.General Concepts (基础概念)•Artificial Intelligence (AI) - 人工智能1)Artificial Intelligence (AI) - 人工智能2)Machine Learning (ML) - 机器学习3)Deep Learning (DL) - 深度学习4)Neural Network - 神经网络5)Natural Language Processing (NLP) - 自然语言处理6)Computer Vision - 计算机视觉7)Robotics - 机器人技术8)Speech Recognition - 语音识别9)Expert Systems - 专家系统10)Knowledge Representation - 知识表示11)Pattern Recognition - 模式识别12)Cognitive Computing - 认知计算13)Autonomous Systems - 自主系统14)Human-Machine Interaction - 人机交互15)Intelligent Agents - 智能代理16)Machine Translation - 机器翻译17)Swarm Intelligence - 群体智能18)Genetic Algorithms - 遗传算法19)Fuzzy Logic - 模糊逻辑20)Reinforcement Learning - 强化学习•Machine Learning (ML) - 机器学习1)Machine Learning (ML) - 机器学习2)Artificial Neural Network - 人工神经网络3)Deep Learning - 深度学习4)Supervised Learning - 有监督学习5)Unsupervised Learning - 无监督学习6)Reinforcement Learning - 强化学习7)Semi-Supervised Learning - 半监督学习8)Training Data - 训练数据9)Test Data - 测试数据10)Validation Data - 验证数据11)Feature - 特征12)Label - 标签13)Model - 模型14)Algorithm - 算法15)Regression - 回归16)Classification - 分类17)Clustering - 聚类18)Dimensionality Reduction - 降维19)Overfitting - 过拟合20)Underfitting - 欠拟合•Deep Learning (DL) - 深度学习1)Deep Learning - 深度学习2)Neural Network - 神经网络3)Artificial Neural Network (ANN) - 人工神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Autoencoder - 自编码器9)Generative Adversarial Network (GAN) - 生成对抗网络10)Transfer Learning - 迁移学习11)Pre-trained Model - 预训练模型12)Fine-tuning - 微调13)Feature Extraction - 特征提取14)Activation Function - 激活函数15)Loss Function - 损失函数16)Gradient Descent - 梯度下降17)Backpropagation - 反向传播18)Epoch - 训练周期19)Batch Size - 批量大小20)Dropout - 丢弃法•Neural Network - 神经网络1)Neural Network - 神经网络2)Artificial Neural Network (ANN) - 人工神经网络3)Deep Neural Network (DNN) - 深度神经网络4)Convolutional Neural Network (CNN) - 卷积神经网络5)Recurrent Neural Network (RNN) - 循环神经网络6)Long Short-Term Memory (LSTM) - 长短期记忆网络7)Gated Recurrent Unit (GRU) - 门控循环单元8)Feedforward Neural Network - 前馈神经网络9)Multi-layer Perceptron (MLP) - 多层感知器10)Radial Basis Function Network (RBFN) - 径向基函数网络11)Hopfield Network - 霍普菲尔德网络12)Boltzmann Machine - 玻尔兹曼机13)Autoencoder - 自编码器14)Spiking Neural Network (SNN) - 脉冲神经网络15)Self-organizing Map (SOM) - 自组织映射16)Restricted Boltzmann Machine (RBM) - 受限玻尔兹曼机17)Hebbian Learning - 海比安学习18)Competitive Learning - 竞争学习19)Neuroevolutionary - 神经进化20)Neuron - 神经元•Algorithm - 算法1)Algorithm - 算法2)Supervised Learning Algorithm - 有监督学习算法3)Unsupervised Learning Algorithm - 无监督学习算法4)Reinforcement Learning Algorithm - 强化学习算法5)Classification Algorithm - 分类算法6)Regression Algorithm - 回归算法7)Clustering Algorithm - 聚类算法8)Dimensionality Reduction Algorithm - 降维算法9)Decision Tree Algorithm - 决策树算法10)Random Forest Algorithm - 随机森林算法11)Support Vector Machine (SVM) Algorithm - 支持向量机算法12)K-Nearest Neighbors (KNN) Algorithm - K近邻算法13)Naive Bayes Algorithm - 朴素贝叶斯算法14)Gradient Descent Algorithm - 梯度下降算法15)Genetic Algorithm - 遗传算法16)Neural Network Algorithm - 神经网络算法17)Deep Learning Algorithm - 深度学习算法18)Ensemble Learning Algorithm - 集成学习算法19)Reinforcement Learning Algorithm - 强化学习算法20)Metaheuristic Algorithm - 元启发式算法•Model - 模型1)Model - 模型2)Machine Learning Model - 机器学习模型3)Artificial Intelligence Model - 人工智能模型4)Predictive Model - 预测模型5)Classification Model - 分类模型6)Regression Model - 回归模型7)Generative Model - 生成模型8)Discriminative Model - 判别模型9)Probabilistic Model - 概率模型10)Statistical Model - 统计模型11)Neural Network Model - 神经网络模型12)Deep Learning Model - 深度学习模型13)Ensemble Model - 集成模型14)Reinforcement Learning Model - 强化学习模型15)Support Vector Machine (SVM) Model - 支持向量机模型16)Decision Tree Model - 决策树模型17)Random Forest Model - 随机森林模型18)Naive Bayes Model - 朴素贝叶斯模型19)Autoencoder Model - 自编码器模型20)Convolutional Neural Network (CNN) Model - 卷积神经网络模型•Dataset - 数据集1)Dataset - 数据集2)Training Dataset - 训练数据集3)Test Dataset - 测试数据集4)Validation Dataset - 验证数据集5)Balanced Dataset - 平衡数据集6)Imbalanced Dataset - 不平衡数据集7)Synthetic Dataset - 合成数据集8)Benchmark Dataset - 基准数据集9)Open Dataset - 开放数据集10)Labeled Dataset - 标记数据集11)Unlabeled Dataset - 未标记数据集12)Semi-Supervised Dataset - 半监督数据集13)Multiclass Dataset - 多分类数据集14)Feature Set - 特征集15)Data Augmentation - 数据增强16)Data Preprocessing - 数据预处理17)Missing Data - 缺失数据18)Outlier Detection - 异常值检测19)Data Imputation - 数据插补20)Metadata - 元数据•Training - 训练1)Training - 训练2)Training Data - 训练数据3)Training Phase - 训练阶段4)Training Set - 训练集5)Training Examples - 训练样本6)Training Instance - 训练实例7)Training Algorithm - 训练算法8)Training Model - 训练模型9)Training Process - 训练过程10)Training Loss - 训练损失11)Training Epoch - 训练周期12)Training Batch - 训练批次13)Online Training - 在线训练14)Offline Training - 离线训练15)Continuous Training - 连续训练16)Transfer Learning - 迁移学习17)Fine-Tuning - 微调18)Curriculum Learning - 课程学习19)Self-Supervised Learning - 自监督学习20)Active Learning - 主动学习•Testing - 测试1)Testing - 测试2)Test Data - 测试数据3)Test Set - 测试集4)Test Examples - 测试样本5)Test Instance - 测试实例6)Test Phase - 测试阶段7)Test Accuracy - 测试准确率8)Test Loss - 测试损失9)Test Error - 测试错误10)Test Metrics - 测试指标11)Test Suite - 测试套件12)Test Case - 测试用例13)Test Coverage - 测试覆盖率14)Cross-Validation - 交叉验证15)Holdout Validation - 留出验证16)K-Fold Cross-Validation - K折交叉验证17)Stratified Cross-Validation - 分层交叉验证18)Test Driven Development (TDD) - 测试驱动开发19)A/B Testing - A/B 测试20)Model Evaluation - 模型评估•Validation - 验证1)Validation - 验证2)Validation Data - 验证数据3)Validation Set - 验证集4)Validation Examples - 验证样本5)Validation Instance - 验证实例6)Validation Phase - 验证阶段7)Validation Accuracy - 验证准确率8)Validation Loss - 验证损失9)Validation Error - 验证错误10)Validation Metrics - 验证指标11)Cross-Validation - 交叉验证12)Holdout Validation - 留出验证13)K-Fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation - 留一法交叉验证16)Validation Curve - 验证曲线17)Hyperparameter Validation - 超参数验证18)Model Validation - 模型验证19)Early Stopping - 提前停止20)Validation Strategy - 验证策略•Supervised Learning - 有监督学习1)Supervised Learning - 有监督学习2)Label - 标签3)Feature - 特征4)Target - 目标5)Training Labels - 训练标签6)Training Features - 训练特征7)Training Targets - 训练目标8)Training Examples - 训练样本9)Training Instance - 训练实例10)Regression - 回归11)Classification - 分类12)Predictor - 预测器13)Regression Model - 回归模型14)Classifier - 分类器15)Decision Tree - 决策树16)Support Vector Machine (SVM) - 支持向量机17)Neural Network - 神经网络18)Feature Engineering - 特征工程19)Model Evaluation - 模型评估20)Overfitting - 过拟合21)Underfitting - 欠拟合22)Bias-Variance Tradeoff - 偏差-方差权衡•Unsupervised Learning - 无监督学习1)Unsupervised Learning - 无监督学习2)Clustering - 聚类3)Dimensionality Reduction - 降维4)Anomaly Detection - 异常检测5)Association Rule Learning - 关联规则学习6)Feature Extraction - 特征提取7)Feature Selection - 特征选择8)K-Means - K均值9)Hierarchical Clustering - 层次聚类10)Density-Based Clustering - 基于密度的聚类11)Principal Component Analysis (PCA) - 主成分分析12)Independent Component Analysis (ICA) - 独立成分分析13)T-distributed Stochastic Neighbor Embedding (t-SNE) - t分布随机邻居嵌入14)Gaussian Mixture Model (GMM) - 高斯混合模型15)Self-Organizing Maps (SOM) - 自组织映射16)Autoencoder - 自动编码器17)Latent Variable - 潜变量18)Data Preprocessing - 数据预处理19)Outlier Detection - 异常值检测20)Clustering Algorithm - 聚类算法•Reinforcement Learning - 强化学习1)Reinforcement Learning - 强化学习2)Agent - 代理3)Environment - 环境4)State - 状态5)Action - 动作6)Reward - 奖励7)Policy - 策略8)Value Function - 值函数9)Q-Learning - Q学习10)Deep Q-Network (DQN) - 深度Q网络11)Policy Gradient - 策略梯度12)Actor-Critic - 演员-评论家13)Exploration - 探索14)Exploitation - 开发15)Temporal Difference (TD) - 时间差分16)Markov Decision Process (MDP) - 马尔可夫决策过程17)State-Action-Reward-State-Action (SARSA) - 状态-动作-奖励-状态-动作18)Policy Iteration - 策略迭代19)Value Iteration - 值迭代20)Monte Carlo Methods - 蒙特卡洛方法•Semi-Supervised Learning - 半监督学习1)Semi-Supervised Learning - 半监督学习2)Labeled Data - 有标签数据3)Unlabeled Data - 无标签数据4)Label Propagation - 标签传播5)Self-Training - 自训练6)Co-Training - 协同训练7)Transudative Learning - 传导学习8)Inductive Learning - 归纳学习9)Manifold Regularization - 流形正则化10)Graph-based Methods - 基于图的方法11)Cluster Assumption - 聚类假设12)Low-Density Separation - 低密度分离13)Semi-Supervised Support Vector Machines (S3VM) - 半监督支持向量机14)Expectation-Maximization (EM) - 期望最大化15)Co-EM - 协同期望最大化16)Entropy-Regularized EM - 熵正则化EM17)Mean Teacher - 平均教师18)Virtual Adversarial Training - 虚拟对抗训练19)Tri-training - 三重训练20)Mix Match - 混合匹配•Feature - 特征1)Feature - 特征2)Feature Engineering - 特征工程3)Feature Extraction - 特征提取4)Feature Selection - 特征选择5)Input Features - 输入特征6)Output Features - 输出特征7)Feature Vector - 特征向量8)Feature Space - 特征空间9)Feature Representation - 特征表示10)Feature Transformation - 特征转换11)Feature Importance - 特征重要性12)Feature Scaling - 特征缩放13)Feature Normalization - 特征归一化14)Feature Encoding - 特征编码15)Feature Fusion - 特征融合16)Feature Dimensionality Reduction - 特征维度减少17)Continuous Feature - 连续特征18)Categorical Feature - 分类特征19)Nominal Feature - 名义特征20)Ordinal Feature - 有序特征•Label - 标签1)Label - 标签2)Labeling - 标注3)Ground Truth - 地面真值4)Class Label - 类别标签5)Target Variable - 目标变量6)Labeling Scheme - 标注方案7)Multi-class Labeling - 多类别标注8)Binary Labeling - 二分类标注9)Label Noise - 标签噪声10)Labeling Error - 标注错误11)Label Propagation - 标签传播12)Unlabeled Data - 无标签数据13)Labeled Data - 有标签数据14)Semi-supervised Learning - 半监督学习15)Active Learning - 主动学习16)Weakly Supervised Learning - 弱监督学习17)Noisy Label Learning - 噪声标签学习18)Self-training - 自训练19)Crowdsourcing Labeling - 众包标注20)Label Smoothing - 标签平滑化•Prediction - 预测1)Prediction - 预测2)Forecasting - 预测3)Regression - 回归4)Classification - 分类5)Time Series Prediction - 时间序列预测6)Forecast Accuracy - 预测准确性7)Predictive Modeling - 预测建模8)Predictive Analytics - 预测分析9)Forecasting Method - 预测方法10)Predictive Performance - 预测性能11)Predictive Power - 预测能力12)Prediction Error - 预测误差13)Prediction Interval - 预测区间14)Prediction Model - 预测模型15)Predictive Uncertainty - 预测不确定性16)Forecast Horizon - 预测时间跨度17)Predictive Maintenance - 预测性维护18)Predictive Policing - 预测式警务19)Predictive Healthcare - 预测性医疗20)Predictive Maintenance - 预测性维护•Classification - 分类1)Classification - 分类2)Classifier - 分类器3)Class - 类别4)Classify - 对数据进行分类5)Class Label - 类别标签6)Binary Classification - 二元分类7)Multiclass Classification - 多类分类8)Class Probability - 类别概率9)Decision Boundary - 决策边界10)Decision Tree - 决策树11)Support Vector Machine (SVM) - 支持向量机12)K-Nearest Neighbors (KNN) - K最近邻算法13)Naive Bayes - 朴素贝叶斯14)Logistic Regression - 逻辑回归15)Random Forest - 随机森林16)Neural Network - 神经网络17)SoftMax Function - SoftMax函数18)One-vs-All (One-vs-Rest) - 一对多(一对剩余)19)Ensemble Learning - 集成学习20)Confusion Matrix - 混淆矩阵•Regression - 回归1)Regression Analysis - 回归分析2)Linear Regression - 线性回归3)Multiple Regression - 多元回归4)Polynomial Regression - 多项式回归5)Logistic Regression - 逻辑回归6)Ridge Regression - 岭回归7)Lasso Regression - Lasso回归8)Elastic Net Regression - 弹性网络回归9)Regression Coefficients - 回归系数10)Residuals - 残差11)Ordinary Least Squares (OLS) - 普通最小二乘法12)Ridge Regression Coefficient - 岭回归系数13)Lasso Regression Coefficient - Lasso回归系数14)Elastic Net Regression Coefficient - 弹性网络回归系数15)Regression Line - 回归线16)Prediction Error - 预测误差17)Regression Model - 回归模型18)Nonlinear Regression - 非线性回归19)Generalized Linear Models (GLM) - 广义线性模型20)Coefficient of Determination (R-squared) - 决定系数21)F-test - F检验22)Homoscedasticity - 同方差性23)Heteroscedasticity - 异方差性24)Autocorrelation - 自相关25)Multicollinearity - 多重共线性26)Outliers - 异常值27)Cross-validation - 交叉验证28)Feature Selection - 特征选择29)Feature Engineering - 特征工程30)Regularization - 正则化2.Neural Networks and Deep Learning (神经网络与深度学习)•Convolutional Neural Network (CNN) - 卷积神经网络1)Convolutional Neural Network (CNN) - 卷积神经网络2)Convolution Layer - 卷积层3)Feature Map - 特征图4)Convolution Operation - 卷积操作5)Stride - 步幅6)Padding - 填充7)Pooling Layer - 池化层8)Max Pooling - 最大池化9)Average Pooling - 平均池化10)Fully Connected Layer - 全连接层11)Activation Function - 激活函数12)Rectified Linear Unit (ReLU) - 线性修正单元13)Dropout - 随机失活14)Batch Normalization - 批量归一化15)Transfer Learning - 迁移学习16)Fine-Tuning - 微调17)Image Classification - 图像分类18)Object Detection - 物体检测19)Semantic Segmentation - 语义分割20)Instance Segmentation - 实例分割21)Generative Adversarial Network (GAN) - 生成对抗网络22)Image Generation - 图像生成23)Style Transfer - 风格迁移24)Convolutional Autoencoder - 卷积自编码器25)Recurrent Neural Network (RNN) - 循环神经网络•Recurrent Neural Network (RNN) - 循环神经网络1)Recurrent Neural Network (RNN) - 循环神经网络2)Long Short-Term Memory (LSTM) - 长短期记忆网络3)Gated Recurrent Unit (GRU) - 门控循环单元4)Sequence Modeling - 序列建模5)Time Series Prediction - 时间序列预测6)Natural Language Processing (NLP) - 自然语言处理7)Text Generation - 文本生成8)Sentiment Analysis - 情感分析9)Named Entity Recognition (NER) - 命名实体识别10)Part-of-Speech Tagging (POS Tagging) - 词性标注11)Sequence-to-Sequence (Seq2Seq) - 序列到序列12)Attention Mechanism - 注意力机制13)Encoder-Decoder Architecture - 编码器-解码器架构14)Bidirectional RNN - 双向循环神经网络15)Teacher Forcing - 强制教师法16)Backpropagation Through Time (BPTT) - 通过时间的反向传播17)Vanishing Gradient Problem - 梯度消失问题18)Exploding Gradient Problem - 梯度爆炸问题19)Language Modeling - 语言建模20)Speech Recognition - 语音识别•Long Short-Term Memory (LSTM) - 长短期记忆网络1)Long Short-Term Memory (LSTM) - 长短期记忆网络2)Cell State - 细胞状态3)Hidden State - 隐藏状态4)Forget Gate - 遗忘门5)Input Gate - 输入门6)Output Gate - 输出门7)Peephole Connections - 窥视孔连接8)Gated Recurrent Unit (GRU) - 门控循环单元9)Vanishing Gradient Problem - 梯度消失问题10)Exploding Gradient Problem - 梯度爆炸问题11)Sequence Modeling - 序列建模12)Time Series Prediction - 时间序列预测13)Natural Language Processing (NLP) - 自然语言处理14)Text Generation - 文本生成15)Sentiment Analysis - 情感分析16)Named Entity Recognition (NER) - 命名实体识别17)Part-of-Speech Tagging (POS Tagging) - 词性标注18)Attention Mechanism - 注意力机制19)Encoder-Decoder Architecture - 编码器-解码器架构20)Bidirectional LSTM - 双向长短期记忆网络•Attention Mechanism - 注意力机制1)Attention Mechanism - 注意力机制2)Self-Attention - 自注意力3)Multi-Head Attention - 多头注意力4)Transformer - 变换器5)Query - 查询6)Key - 键7)Value - 值8)Query-Value Attention - 查询-值注意力9)Dot-Product Attention - 点积注意力10)Scaled Dot-Product Attention - 缩放点积注意力11)Additive Attention - 加性注意力12)Context Vector - 上下文向量13)Attention Score - 注意力分数14)SoftMax Function - SoftMax函数15)Attention Weight - 注意力权重16)Global Attention - 全局注意力17)Local Attention - 局部注意力18)Positional Encoding - 位置编码19)Encoder-Decoder Attention - 编码器-解码器注意力20)Cross-Modal Attention - 跨模态注意力•Generative Adversarial Network (GAN) - 生成对抗网络1)Generative Adversarial Network (GAN) - 生成对抗网络2)Generator - 生成器3)Discriminator - 判别器4)Adversarial Training - 对抗训练5)Minimax Game - 极小极大博弈6)Nash Equilibrium - 纳什均衡7)Mode Collapse - 模式崩溃8)Training Stability - 训练稳定性9)Loss Function - 损失函数10)Discriminative Loss - 判别损失11)Generative Loss - 生成损失12)Wasserstein GAN (WGAN) - Wasserstein GAN（WGAN）13)Deep Convolutional GAN (DCGAN) - 深度卷积生成对抗网络（DCGAN）14)Conditional GAN (c GAN) - 条件生成对抗网络（c GAN）15)Style GAN - 风格生成对抗网络16)Cycle GAN - 循环生成对抗网络17)Progressive Growing GAN (PGGAN) - 渐进式增长生成对抗网络（PGGAN）18)Self-Attention GAN (SAGAN) - 自注意力生成对抗网络（SAGAN）19)Big GAN - 大规模生成对抗网络20)Adversarial Examples - 对抗样本•Encoder-Decoder - 编码器-解码器1)Encoder-Decoder Architecture - 编码器-解码器架构2)Encoder - 编码器3)Decoder - 解码器4)Sequence-to-Sequence Model (Seq2Seq) - 序列到序列模型5)State Vector - 状态向量6)Context Vector - 上下文向量7)Hidden State - 隐藏状态8)Attention Mechanism - 注意力机制9)Teacher Forcing - 强制教师法10)Beam Search - 束搜索11)Recurrent Neural Network (RNN) - 循环神经网络12)Long Short-Term Memory (LSTM) - 长短期记忆网络13)Gated Recurrent Unit (GRU) - 门控循环单元14)Bidirectional Encoder - 双向编码器15)Greedy Decoding - 贪婪解码16)Masking - 遮盖17)Dropout - 随机失活18)Embedding Layer - 嵌入层19)Cross-Entropy Loss - 交叉熵损失20)Tokenization - 令牌化•Transfer Learning - 迁移学习1)Transfer Learning - 迁移学习2)Source Domain - 源领域3)Target Domain - 目标领域4)Fine-Tuning - 微调5)Domain Adaptation - 领域自适应6)Pre-Trained Model - 预训练模型7)Feature Extraction - 特征提取8)Knowledge Transfer - 知识迁移9)Unsupervised Domain Adaptation - 无监督领域自适应10)Semi-Supervised Domain Adaptation - 半监督领域自适应11)Multi-Task Learning - 多任务学习12)Data Augmentation - 数据增强13)Task Transfer - 任务迁移14)Model Agnostic Meta-Learning (MAML) - 与模型无关的元学习（MAML）15)One-Shot Learning - 单样本学习16)Zero-Shot Learning - 零样本学习17)Few-Shot Learning - 少样本学习18)Knowledge Distillation - 知识蒸馏19)Representation Learning - 表征学习20)Adversarial Transfer Learning - 对抗迁移学习•Pre-trained Models - 预训练模型1)Pre-trained Model - 预训练模型2)Transfer Learning - 迁移学习3)Fine-Tuning - 微调4)Knowledge Transfer - 知识迁移5)Domain Adaptation - 领域自适应6)Feature Extraction - 特征提取7)Representation Learning - 表征学习8)Language Model - 语言模型9)Bidirectional Encoder Representations from Transformers (BERT) - 双向编码器结构转换器10)Generative Pre-trained Transformer (GPT) - 生成式预训练转换器11)Transformer-based Models - 基于转换器的模型12)Masked Language Model (MLM) - 掩蔽语言模型13)Cloze Task - 填空任务14)Tokenization - 令牌化15)Word Embeddings - 词嵌入16)Sentence Embeddings - 句子嵌入17)Contextual Embeddings - 上下文嵌入18)Self-Supervised Learning - 自监督学习19)Large-Scale Pre-trained Models - 大规模预训练模型•Loss Function - 损失函数1)Loss Function - 损失函数2)Mean Squared Error (MSE) - 均方误差3)Mean Absolute Error (MAE) - 平均绝对误差4)Cross-Entropy Loss - 交叉熵损失5)Binary Cross-Entropy Loss - 二元交叉熵损失6)Categorical Cross-Entropy Loss - 分类交叉熵损失7)Hinge Loss - 合页损失8)Huber Loss - Huber损失9)Wasserstein Distance - Wasserstein距离10)Triplet Loss - 三元组损失11)Contrastive Loss - 对比损失12)Dice Loss - Dice损失13)Focal Loss - 焦点损失14)GAN Loss - GAN损失15)Adversarial Loss - 对抗损失16)L1 Loss - L1损失17)L2 Loss - L2损失18)Huber Loss - Huber损失19)Quantile Loss - 分位数损失•Activation Function - 激活函数1)Activation Function - 激活函数2)Sigmoid Function - Sigmoid函数3)Hyperbolic Tangent Function (Tanh) - 双曲正切函数4)Rectified Linear Unit (Re LU) - 矩形线性单元5)Parametric Re LU (P Re LU) - 参数化Re LU6)Exponential Linear Unit (ELU) - 指数线性单元7)Swish Function - Swish函数8)Softplus Function - Soft plus函数9)Softmax Function - SoftMax函数10)Hard Tanh Function - 硬双曲正切函数11)Softsign Function - Softsign函数12)GELU (Gaussian Error Linear Unit) - GELU（高斯误差线性单元）13)Mish Function - Mish函数14)CELU (Continuous Exponential Linear Unit) - CELU（连续指数线性单元）15)Bent Identity Function - 弯曲恒等函数16)Gaussian Error Linear Units (GELUs) - 高斯误差线性单元17)Adaptive Piecewise Linear (APL) - 自适应分段线性函数18)Radial Basis Function (RBF) - 径向基函数•Backpropagation - 反向传播1)Backpropagation - 反向传播2)Gradient Descent - 梯度下降3)Partial Derivative - 偏导数4)Chain Rule - 链式法则5)Forward Pass - 前向传播6)Backward Pass - 反向传播7)Computational Graph - 计算图8)Neural Network - 神经网络9)Loss Function - 损失函数10)Gradient Calculation - 梯度计算11)Weight Update - 权重更新12)Activation Function - 激活函数13)Optimizer - 优化器14)Learning Rate - 学习率15)Mini-Batch Gradient Descent - 小批量梯度下降16)Stochastic Gradient Descent (SGD) - 随机梯度下降17)Batch Gradient Descent - 批量梯度下降18)Momentum - 动量19)Adam Optimizer - Adam优化器20)Learning Rate Decay - 学习率衰减•Gradient Descent - 梯度下降1)Gradient Descent - 梯度下降2)Stochastic Gradient Descent (SGD) - 随机梯度下降3)Mini-Batch Gradient Descent - 小批量梯度下降4)Batch Gradient Descent - 批量梯度下降5)Learning Rate - 学习率6)Momentum - 动量7)Adaptive Moment Estimation (Adam) - 自适应矩估计8)RMSprop - 均方根传播9)Learning Rate Schedule - 学习率调度10)Convergence - 收敛11)Divergence - 发散12)Adagrad - 自适应学习速率方法13)Adadelta - 自适应增量学习率方法14)Adamax - 自适应矩估计的扩展版本15)Nadam - Nesterov Accelerated Adaptive Moment Estimation16)Learning Rate Decay - 学习率衰减17)Step Size - 步长18)Conjugate Gradient Descent - 共轭梯度下降19)Line Search - 线搜索20)Newton's Method - 牛顿法•Learning Rate - 学习率1)Learning Rate - 学习率2)Adaptive Learning Rate - 自适应学习率3)Learning Rate Decay - 学习率衰减4)Initial Learning Rate - 初始学习率5)Step Size - 步长6)Momentum - 动量7)Exponential Decay - 指数衰减8)Annealing - 退火9)Cyclical Learning Rate - 循环学习率10)Learning Rate Schedule - 学习率调度11)Warm-up - 预热12)Learning Rate Policy - 学习率策略13)Learning Rate Annealing - 学习率退火14)Cosine Annealing - 余弦退火15)Gradient Clipping - 梯度裁剪16)Adapting Learning Rate - 适应学习率17)Learning Rate Multiplier - 学习率倍增器18)Learning Rate Reduction - 学习率降低19)Learning Rate Update - 学习率更新20)Scheduled Learning Rate - 定期学习率•Batch Size - 批量大小1)Batch Size - 批量大小2)Mini-Batch - 小批量3)Batch Gradient Descent - 批量梯度下降4)Stochastic Gradient Descent (SGD) - 随机梯度下降5)Mini-Batch Gradient Descent - 小批量梯度下降6)Online Learning - 在线学习7)Full-Batch - 全批量8)Data Batch - 数据批次9)Training Batch - 训练批次10)Batch Normalization - 批量归一化11)Batch-wise Optimization - 批量优化12)Batch Processing - 批量处理13)Batch Sampling - 批量采样14)Adaptive Batch Size - 自适应批量大小15)Batch Splitting - 批量分割16)Dynamic Batch Size - 动态批量大小17)Fixed Batch Size - 固定批量大小18)Batch-wise Inference - 批量推理19)Batch-wise Training - 批量训练20)Batch Shuffling - 批量洗牌•Epoch - 训练周期1)Training Epoch - 训练周期2)Epoch Size - 周期大小3)Early Stopping - 提前停止4)Validation Set - 验证集5)Training Set - 训练集6)Test Set - 测试集7)Overfitting - 过拟合8)Underfitting - 欠拟合9)Model Evaluation - 模型评估10)Model Selection - 模型选择11)Hyperparameter Tuning - 超参数调优12)Cross-Validation - 交叉验证13)K-fold Cross-Validation - K折交叉验证14)Stratified Cross-Validation - 分层交叉验证15)Leave-One-Out Cross-Validation (LOOCV) - 留一法交叉验证16)Grid Search - 网格搜索17)Random Search - 随机搜索18)Model Complexity - 模型复杂度19)Learning Curve - 学习曲线20)Convergence - 收敛3.Machine Learning Techniques and Algorithms (机器学习技术与算法)•Decision Tree - 决策树1)Decision Tree - 决策树2)Node - 节点3)Root Node - 根节点4)Leaf Node - 叶节点5)Internal Node - 内部节点6)Splitting Criterion - 分裂准则7)Gini Impurity - 基尼不纯度8)Entropy - 熵9)Information Gain - 信息增益10)Gain Ratio - 增益率11)Pruning - 剪枝12)Recursive Partitioning - 递归分割13)CART (Classification and Regression Trees) - 分类回归树14)ID3 (Iterative Dichotomiser 3) - 迭代二叉树315)C4.5 (successor of ID3) - C4.5（ID3的后继者）16)C5.0 (successor of C4.5) - C5.0（C4.5的后继者）17)Split Point - 分裂点18)Decision Boundary - 决策边界19)Pruned Tree - 剪枝后的树20)Decision Tree Ensemble - 决策树集成•Random Forest - 随机森林1)Random Forest - 随机森林2)Ensemble Learning - 集成学习3)Bootstrap Sampling - 自助采样4)Bagging (Bootstrap Aggregating) - 装袋法5)Out-of-Bag (OOB) Error - 袋外误差6)Feature Subset - 特征子集7)Decision Tree - 决策树8)Base Estimator - 基础估计器9)Tree Depth - 树深度10)Randomization - 随机化11)Majority Voting - 多数投票12)Feature Importance - 特征重要性13)OOB Score - 袋外得分14)Forest Size - 森林大小15)Max Features - 最大特征数16)Min Samples Split - 最小分裂样本数17)Min Samples Leaf - 最小叶节点样本数18)Gini Impurity - 基尼不纯度19)Entropy - 熵20)Variable Importance - 变量重要性•Support Vector Machine (SVM) - 支持向量机1)Support Vector Machine (SVM) - 支持向量机2)Hyperplane - 超平面3)Kernel Trick - 核技巧4)Kernel Function - 核函数5)Margin - 间隔6)Support Vectors - 支持向量7)Decision Boundary - 决策边界8)Maximum Margin Classifier - 最大间隔分类器9)Soft Margin Classifier - 软间隔分类器10) C Parameter - C参数11)Radial Basis Function (RBF) Kernel - 径向基函数核12)Polynomial Kernel - 多项式核13)Linear Kernel - 线性核14)Quadratic Kernel - 二次核15)Gaussian Kernel - 高斯核16)Regularization - 正则化17)Dual Problem - 对偶问题18)Primal Problem - 原始问题19)Kernelized SVM - 核化支持向量机20)Multiclass SVM - 多类支持向量机•K-Nearest Neighbors (KNN) - K-最近邻1)K-Nearest Neighbors (KNN) - K-最近邻2)Nearest Neighbor - 最近邻3)Distance Metric - 距离度量4)Euclidean Distance - 欧氏距离5)Manhattan Distance - 曼哈顿距离6)Minkowski Distance - 闵可夫斯基距离7)Cosine Similarity - 余弦相似度8)K Value - K值9)Majority Voting - 多数投票10)Weighted KNN - 加权KNN11)Radius Neighbors - 半径邻居12)Ball Tree - 球树13)KD Tree - KD树14)Locality-Sensitive Hashing (LSH) - 局部敏感哈希15)Curse of Dimensionality - 维度灾难16)Class Label - 类标签17)Training Set - 训练集18)Test Set - 测试集19)Validation Set - 验证集20)Cross-Validation - 交叉验证•Naive Bayes - 朴素贝叶斯1)Naive Bayes - 朴素贝叶斯2)Bayes' Theorem - 贝叶斯定理3)Prior Probability - 先验概率4)Posterior Probability - 后验概率5)Likelihood - 似然6)Class Conditional Probability - 类条件概率7)Feature Independence Assumption - 特征独立假设8)Multinomial Naive Bayes - 多项式朴素贝叶斯9)Gaussian Naive Bayes - 高斯朴素贝叶斯10)Bernoulli Naive Bayes - 伯努利朴素贝叶斯11)Laplace Smoothing - 拉普拉斯平滑12)Add-One Smoothing - 加一平滑13)Maximum A Posteriori (MAP) - 最大后验概率14)Maximum Likelihood Estimation (MLE) - 最大似然估计15)Classification - 分类16)Feature Vectors - 特征向量17)Training Set - 训练集18)Test Set - 测试集19)Class Label - 类标签20)Confusion Matrix - 混淆矩阵•Clustering - 聚类1)Clustering - 聚类2)Centroid - 质心3)Cluster Analysis - 聚类分析4)Partitioning Clustering - 划分式聚类5)Hierarchical Clustering - 层次聚类6)Density-Based Clustering - 基于密度的聚类7)K-Means Clustering - K均值聚类8)K-Medoids Clustering - K中心点聚类9)DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - 基于密度的空间聚类算法10)Agglomerative Clustering - 聚合式聚类11)Dendrogram - 系统树图12)Silhouette Score - 轮廓系数13)Elbow Method - 肘部法则14)Clustering Validation - 聚类验证15)Intra-cluster Distance - 类内距离16)Inter-cluster Distance - 类间距离17)Cluster Cohesion - 类内连贯性18)Cluster Separation - 类间分离度19)Cluster Assignment - 聚类分配20)Cluster Label - 聚类标签•K-Means - K-均值1)K-Means - K-均值2)Centroid - 质心3)Cluster - 聚类4)Cluster Center - 聚类中心5)Cluster Assignment - 聚类分配6)Cluster Analysis - 聚类分析7)K Value - K值8)Elbow Method - 肘部法则9)Inertia - 惯性10)Silhouette Score - 轮廓系数11)Convergence - 收敛12)Initialization - 初始化13)Euclidean Distance - 欧氏距离14)Manhattan Distance - 曼哈顿距离15)Distance Metric - 距离度量16)Cluster Radius - 聚类半径17)Within-Cluster Variation - 类内变异18)Cluster Quality - 聚类质量19)Clustering Algorithm - 聚类算法20)Clustering Validation - 聚类验证•Dimensionality Reduction - 降维1)Dimensionality Reduction - 降维2)Feature Extraction - 特征提取3)Feature Selection - 特征选择4)Principal Component Analysis (PCA) - 主成分分析5)Singular Value Decomposition (SVD) - 奇异值分解6)Linear Discriminant Analysis (LDA) - 线性判别分析7)t-Distributed Stochastic Neighbor Embedding (t-SNE) - t-分布随机邻域嵌入8)Autoencoder - 自编码器9)Manifold Learning - 流形学习10)Locally Linear Embedding (LLE) - 局部线性嵌入11)Isomap - 等度量映射12)Uniform Manifold Approximation and Projection (UMAP) - 均匀流形逼近与投影13)Kernel PCA - 核主成分分析14)Non-negative Matrix Factorization (NMF) - 非负矩阵分解15)Independent Component Analysis (ICA) - 独立成分分析16)Variational Autoencoder (VAE) - 变分自编码器17)Sparse Coding - 稀疏编码18)Random Projection - 随机投影19)Neighborhood Preserving Embedding (NPE) - 保持邻域结构的嵌入20)Curvilinear Component Analysis (CCA) - 曲线成分分析•Principal Component Analysis (PCA) - 主成分分析1)Principal Component Analysis (PCA) - 主成分分析2)Eigenvector - 特征向量3)Eigenvalue - 特征值4)Covariance Matrix - 协方差矩阵。

火山岩岩性识别方法新动向

火山岩岩性识别方法新动向X张征辉(中国地质大学(北京),北京　100083) 摘　要:火山岩油气藏已成为我国新型油气藏勘探开发的重要对象,火山岩岩性的识别既是火山岩油气藏研究的基础,又是油藏研究的难点。

现在火山岩岩性识别的主流方法有综合各种方法之长的趋势,本文着重介绍了FMI 结合ECS 综合识别岩性法、综合交汇图法和PCA+SOM 神经网络法,均是采用融合多种识别方法之长进行岩性识别的方法,这些方法在各自的适用条件下比以往方法能更准确的识别出火山岩岩性。

关键词:火山岩;岩性识别;FMI ;ECS ;交汇图;PCA ;神经网络中图分类号:P588.14∶P584　文献标识码:A 文章编号:1006—7981(2012)06—0142—03 自19世纪末美国加利福尼亚州的圣华金盆地首次发现火山岩油气藏以来,相继在100多个国家/地区发现了火山岩油气藏或与火山作用有关的油气显示[1][4]。

在我国,常规油气田勘探领域已基本调查完毕,取得重大突破的可能性较小,因此寻找油气勘探新领域成为迫切需要解决的问题[2]。

火山岩是盆地早期充填的重要组成部分,体积约占25%[10],从2007年世界范围内的油气探明储量来看,来自火山岩的储量仅占全球油气总储量的1%左右,勘探潜力巨大。

自上世纪50年代在准格尔盆地发现油气以来,中国已在11个盆地中陆续发现了火山岩油气田[2]。

特别是2000年以来,我国火山岩油气勘探成果显著,相继在渤海湾盆地、松辽盆地、二连盆地、准噶尔盆地、塔里木盆地、四川盆地中获得突破性进展。

火山岩油气田已经成为我国油气田的重要组成部分和重要勘探目标。

根据李宁,乔德新等人研究成果,火山岩的岩性识别是火山岩油气田测井处理的基础,岩性识别也是油气藏储层研究工作的基础,因此火山岩油气藏中岩性的识别就成为了一个特别重要的一个环节。

由于火山岩机构多样,火山岩石的矿物盛饭、结构、构造相当复杂,火山岩的非均质性特别明显,所以岩性识别难度就相当大。

DAMA-CDGA(数据治理工程师)-重点章节习题-第14章(大数据和数据科学)

数据治理工程师 CDGA 认证考试习题集第十四章大数据和数据科学（重点章节）1. 数据科学家开展工作依赖于哪些要素（）A. 丰富的数据源B. 信息组织和分析C. 展示发现和数据洞察D. 以上全部2. 那些从数据中探究、研发预测模型、机器学习模型、规范性模型和分析方法并将研发结果进行部署供相关方分析的人，被称为（）A. CDO 首席数据官B. 数据分析师C. 数据科学家D. 数据架构师3. 早期，人们通过 3V 来定义大数据含义的特征，请从下列选项中选择不包含在 3V 中的一个选项。

（）A. 数据量大B. 数据粘度大1C. 数据更新频繁D. 数据类型多样4. 尝试通过概率估计来预测未来结果的应用程序称为？（）A.维度分析B.预测分析C.即时报告D.描述性分析5. 以下哪种技术已经成为面向数据科学的大数据集分析标准平台。

（）A、MPP 技术。

B、Hadoop 技术。

C、Hbase 技术。

D、Redis 技术。

6. 以下哪一项是提升一个组织大数据和数据科学能力的最大业务驱动力。

（）A、提升业务效率。

B、期望抓住从多种流程生成的数据集中发现的商机。

C、保障数据合规与安全。

D、加强业务管控。

7、以下选项中不属于数据挖掘经常使用的技术是（）A.剖析（Profiling）B.向上卷积（Roll-up）C.数据缩减（Data reduction）D.自组织映射（Self-organizing maps）8、ETL 的作用主要体现在（）A.构建数据集市B.管理数据仓库C.把数据转换为信息、知识D.数据库数据存储9、关于数据仓库和数据湖的主要差别，以下哪项描述是不正确的（）A.存储数据类型和数据结构化流程不同B.主要提供的服务不同C.面向主要用户不同D.应用侧重点不同10、定义大数据战略和业务需求，应该考虑提供数据的及时性和范围，许多元素可以实施提供，也可以定时提供快照，甚至可以整合和汇总，其中流式计算越来越成为热点，以下不属于流式计算框架的是：（）A.StromB.FlinkC.HadoopD.Spark11、MapReduce 模型有三个主要步骤（）A.剖析、关联、聚类B.提取、转换、加载C.映射、修正、转换D.映射、洗牌、归并数据治理工程师 CDGA 认证考试习题答案第十四章大数据和数据科学（重点章节）1. 正确答案：D【答案解析】详见书本 P388-389 页。

基于分布的无监督学习算法研究

基于分布的无监督学习算法研究无监督学习是机器学习的一种重要分支，它通过从数据中发现隐藏的模式和结构，来构建模型和进行预测。

在无监督学习中，没有标签或类别信息可用于指导算法，因此算法需要自主地从数据中提取有用的信息。

基于分布的无监督学习算法是一类重要的方法，它通过对数据进行概率建模来进行模式发现和聚类。

在基于分布的无监督学习中，我们假设数据是从一个潜在分布中生成的。

我们希望通过对这个潜在分布进行建模来了解数据生成机制，并从中发现有用的结构。

这种方法广泛应用于许多领域，如聚类、降维、异常检测等。

一个经典且常用的基于分布的无监督学习算法是高斯混合模型（Gaussian Mixture Model, GMM）。

GMM假设数据是由多个高斯分布混合而成，并通过最大似然估计来估计每个高斯成分以及其对应权重。

GMM可以有效地对复杂数据进行建模，并且可以应用于聚类、异常检测等任务。

除了GMM之外，另一个重要的基于分布的无监督学习算法是K均值聚类（K-means clustering）。

K均值聚类是一种迭代算法，它将数据点分配到K个簇中，使得同一簇内的数据点之间的距离最小化。

这种算法简单且高效，广泛应用于数据挖掘和图像处理等领域。

近年来，基于深度学习的无监督学习方法取得了显著进展。

深度生成模型（Deep Generative Models）是一类基于分布的无监督学习方法，它通过深度神经网络来建模数据生成过程。

其中最著名的模型之一是变分自编码器（Variational Autoencoder, VAE）。

VAE通过将输入数据映射到一个潜在空间中，并通过最大化潜在空间中样本与原始样本之间的重构误差来训练模型。

VAE不仅可以用于生成新样本，还可以用于降维和特征提取等任务。

除了VAE之外，生成对抗网络（Generative Adversarial Networks, GAN）也是一种非常流行和强大的基于分布的无监督学习方法。

ONE-CLASS-CLASSIFICATION 一分类问题

• Number of Gaussians is defined beforehand; means and covariance can be estimated
Density methods Parzen density estimation
• Also an extension of Gaussian model:
Characteristics of one-class approaches
• Robustness to outliers: * when in a method only the resemblance or distance is optimized, it can therefore be assumed that objects near the threshold are the candidate outlier objects. * for methods where resemblance is optimized for a given threshold, a more advanced method for outliers should be applied in the training set.
Boundary methods K-centers
• General idea: covers the dataset with k small balls with equal radii • To minimize: (maximum distance of all minimum distances between training objects and the centers)
Characteristics of one-class approaches (2)

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DATA MINING WITH SELF-ORGANIZING MAPS:PART I : MAIN STEPSBy Guido Deboeck, Ph.D*Many articles and courses outline principles and the details of algorithms that can be used for data mining e.g. neural networks, genetic algorithms, fuzzy logic. They emphasis technique rather than practice This article summarizes "best practices" in data mining, clustering and visualization of large multi-dimensional data sets in finance, economics or marketing.These best practices are based on the lessons learned from many applications presented in Visual Explorations in Finance with self-organizing maps (Springer-Verlag, 1998), lessons extracted from many papers presented at neural net conferences and the expertise from people who have several years of hands-on experience in applying neural networks in finance and economics. The process described here for data analysis, clustering, visualization, and evaluation can be applied to many applications. As an illustration we will use self-organizing maps(SOM), which is a technique based on unsupervised neural networks that uses competitive learning in order to create a reduced two dimensional representation of a large multi-dimensional data set. Part II of this article will apply the steps outlined here to the problem of assessing country credit risks based on economic, financial and stock market data.Main stepsThe financial, economic and marketing applications of self-organizing maps outlined in Visual Explorations in Finance with self-organizing maps show that there are no specific procedures or optimal methods for applying SOM that are valid for all applications. Similar to the design of other neural network models, to create a self-organizing map, is still an art more than a science.Like for many other approaches the "engineering" aspects of SOM , e.g. for selection of a SOM array; the scaling of the input variables; the initialization of the algorithm; the selection of the neighborhood size, the learning rate; the interpretation and color coding of the map, are easy to obtain. However these are not sufficient for the entire process of data analysis. Hence, we present here a series of steps that details the process of data mining rather than the application of any specific algorithm or technique.Box 1: Main steps in clustering and visualization of dataStep 1.Define the purpose of the analysis;Step 2.Select the data source and -quality;Step 3.Select the data scope and variables;Step 4.Decide how each of the variables will be preprocessed;Step e relevant sample data that are representative for your system;Step 6.Select the clustering and visualization method(s);consider the use of hybrid methods;Step 7.Determine parameters : in case of SOM the desired display size, map ratio,the required degree of detail;Step 8.Tune the output or map for optimal clustering and visualization;Step 9.Interpret the results, check the values of individual nodes and clusters;Step 10.Define or paste appropriate map labels;Step 11.Produce summary results that highlight the differences between clusters;Step 12.Document and evaluate the results.1 Define the purpose of the analysisWithout proper definition of the goals and objectives for the design of a neural network model, supervised or unsupervised, it will be difficult to assess the effectiveness of the outcome. Neural net models can be designed for many different objectives. As shown in several applications the main objectives for design of a neural network or self-organizing map in our case can be for(i) classification, clustering, and/or data reduction;(ii) visualization of the data;(iii) decision-support;(iv) hypothesis testing;(v) monitoring system performance;(vi) lookup of (missing) values;(vii) forecasting.If clustering and visualization are the main objectives, various alternative visualization and clustering methods should be considered. Several traditional statistical methods for clustering and data visualization exist. Combining traditional statistical methods with neural network techniques like SOM may generate better results than the use of one technique by itself. It may also be useful to determine a priori how much data reduction is desired.If decision-support is the main objective then it is essential to define precisely what decisions need to be supported, what is the scope of these decisions, and what is their time frame. For example, predicting the direction of a market is quite different from predicting future price levels.If hypothesis testing is the main objective one needs to define a priori what hypotheses will be tested and what will be the standard for acceptance or rejection. For example, when applying neural networks to banking data the hypothesis may be is there a significant difference between the various banking institutions in various markets around the world.If monitoring systems performance is the objective the goals of the monitoring process need to be defined e.g. monitoring for quality purposes, fault detection, standard compliance. If forecasting is the objective, it is important to spell out what is the forecasting window, the desired accuracy, how will the performance be evaluated. For example, for what time window are the predictions for, should the predictions be accurate in terms of the level or just the direction; will the price predictions be evaluated in terms of the percentage of correct predictions or in terms of the cumulative profit or loss achieved by implementing the predictions in a given period.2. Select the data source and data qualityThee importance of using high quality data can not be underestimated. It is important that the data comes from reputable sources. Good sources of high quality financial and economic data are- national and international agencies (e.g. government statistical offices, the United Nations, specialized agencies of the UN, World Bank, IMF, and the like),- well-established information services (e.g. Bloomberg, Reuters, Telerate, Knight-Ridder, Standard & Poor), or- data base providers (e.g. Value Line, Morningstar, DRI, Moody's, American OnLine, CompuServe etc.) and many others.Data that is freely available on the web may or may not be of high quality. It is therefore advisable to be skeptical about what is freely offered on the web.3. Select the data scope and variablesTo define the data scope in relation to the objectives of the study is important for any kind of analysis. Neural network techniques based on learning techniques and competitive learning in particular may cause laziness and or attempts to "through in the kitchen sink", i.e. use all the available data on a particular subject rather than a selective set relevant to the objectives of the study.Furthermore it is important to use domain expertise, or to collaborate with those that have such expertise. For example, when studying structures in investment data, credit risk data, poverty data, proper analyses cannot be done without domain knowledge.One should also be careful in the selection of the appropriate indicators. Once the data scope has been properly defined, some important tips to remember in selecting variables to be included in the analysis are• do not get wed to your data, learn to discriminate, discard and delete• select only those variables that are meaningful in relation to the objectives• select the variables that are most likely to influence the results• consider to use combinations of variables, such as ratios, time-invariants etc.• use domain expertise or involve in the analysis people who have domain expertise• do not assume that the data is normally distributed• adding of one or more irrelevant variables can dramatically interfere with the cluster recovery• omission of one or more important variables may also affect the results.4. Decide how each of the variables will be preprocessedPre-processing of data is important particularly in neural network design. When pre-processing data for clustering the pre-processing may specifically involve data standardization, -transformations, and setting of priorities.The main reason for data standardization is to scale all data to the same level. Often the data range of each variable varies from column to column. If no preprocessing is applied this may influence the clustering and the ultimate shape of the output map. There are many ways in which data can be standardized. The most are to standardize all data based on the standard deviation. Other methods are to standardize on the basis of the range e.g. z= [x - min(x)] / [max(x)-min(x)]. Some studies have shown that standardizing the data based on the range can be superior in certain cases, in particular if the variance is much smaller than the range.Data transformations can be applied to any or all variables to influence the importance and/or influence of each variable on the final outcome. Transformations may also be used to …equalize“ the histograms. Two typical data transformations are logarithmic and sigmoid. The former squeezes the scale for large values, the latter takes care of outliners. Applying data transformations redefines the internal representation of each variable and should be applied with caution.Setting the priority of a variable to a value greater or lower than one has the same effect as changing the standardization explicitly. By giving a priority to a variable you can provide a weighting of the variables in the mapping process. For example, if in the selection of investment managers, the 'launching date' of a mutual fund is considered less important, then this variable can be given a low priority.5. Use relevant sample data representative for your systemTraining a neural network on a set of sample data will yield better results when using random initial input vectors. By selecting representative input vectors for the training of aSOM map one reduces noise and can obtain a sharper map. This map then can be used for testing on all the remainder input data sets. Furthermore, depending on the applications, the use of input vectors that represent outliners may be of crucial importance for training a SOM. Outliners provide contrasts and can sharpen the differences between clusters. However, this can be to the cost of sensibility for the other parts of the map. If outliners are not representative, they should of course be eliminated.6. Select the clustering and visualization method(s); consider the use of hybrid methodsIn this article we focus on SOM however combining SOM with other methods can yield better results. For example, a hybrid system of SOM and genetic algorithms can improve the performance of trading models; overlaying the results from SOM on top of principal component analysis can improve visualization; combining SOM with a Geographic Information System can improve interpretation.In financial, economic and marketing applications combining SOM with other statistical methods is common practice. A SOM map by itself provides a topological representation of the data which needs to be translated in operational or actionable outcomes. Financial analysts, economists and certainly marketing professionals will want to know what are the main features of the clusters, how they differ from each other, and how to use the newly found structures or patterns for forecasting or decision-support. Thus a SOM map by itself can not be a final outcome.7. Determine the desired display size, shape, and the required degree of detail Bigger maps produce more detail; input vectors are spread out on a larger number of nodes. Smaller maps can contain bigger clusters or more input vectors can cluster on a smaller set of nodes. Which is better ? This will depend on the application and the usage of the map. Smaller is not necessarily better. More detail may be desirable in some cases. In general, smaller numbers of nodes stand for higher generalization, and this may also be useful if the data contains much noise. Higher numbers of nodes normally yield nicer map images but must not be over-interpreted in later use.The key in determining the size of the map will be how the map will be used. A simple analogue would be to compare the use of a country atlas with that of highway or street maps. If using SOM for lookup of information a larger SOM map may be more desirable; however when using SOM to select investment opportunities or investment managers a smaller map that clusters managers and investment opportunities in five to seven categories may be more optimal.8. Tune the output or map for optimal clustering and visualizationOnce a SOM has been trained you can inspect the map by looking at the number of nodes that contain input vectors, the mean values of the nodes and clusters, the number of clusters that were created, and the number of matching input vectors for each cluster. Fine-tuning a map can be done by increasing or reducing the cluster threshold and/or the minimum cluster size. A larger cluster threshold or higher minimum cluster size willreduce the number of clusters, it will increase the coarse-ness of the clustering. Lowering the cluster threshold will show more details of the map.9. Interpret the results, check values of individual nodes and clusters,Once a topological representation of the data is created, it is important to check the validity of the map. This can be done in several ways. Again domain expertise will be a key ingredient. A simple check may consist in printing a list of the input vectors sorted by node or cluster of the map. Another one may be to calculate some simple summary statistics on each cluster.Depending on which software tool is used the mean values of the clusters may be even displayed on the screen. In this case the user can interactively check each cluster and judge whether the summary values make sense. Comparisons of values among nodes and clusters will then allow the user to decide on how more detailed the map needs to be, which data transformation could be needed, how to fine-tune the priority of some components, or what the generalization capability of the map eventually may be. In other words, an interactive capability to check the values for nodes and clusters is important in order to allow the process to be dynamic and to incorporate the user's domain expertise and knowledge about the data.10.Define or pasting appropriate map labelsThe importance and difficulty of defining appropriate labels has been discussed in many articles. When using SOM to classify countries, states or cities, or when using SOM to cluster investment opportunities, companies, or banks the labels to be used are obvious: each input vector can be extended with an appropriate or abbreviate name of the country, state, city, security, company or bank it represents. When using SOM for process control labeling may be restricted to a few input vectors, picking on those that represent failures, or idle states. When using SOM to classify wines or whiskeys, multiple labels may be necessary to identify the country, region, vineyard or distillery. In sum, flexibility in automatic labeling of nodes or clusters from the input data vectors is of crucial importance. This automatic labeling capability is of particular importance for finance, economic and marketing applications.11. Produce a summary of the map results that highlight the differences between clustersThe production of summary statistics may be automatic or manual depending on which software tool is used for SOM. Newer software packages have built-in capabilities for automatic production of summary statistics. This has advantages over software tools that do not provide any post-processing capability. In finance, economics and marketing, post-processing of SOM results, information extraction of value added, and how SOM results can be used is very important. A post-processing capability that allows to create summary statistics for each node and each cluster showing at the minimum the mean, standard deviation, minimum, maximum value, and the sum of the input vectors is a great advantage.12.Document and evaluate resultsFor SOM to be useful in finance, economics and marketing, it is essential to demonstrate its value added. "Look Mom what a nice picture I made" will just not fly in boardrooms, management meetings or strategic marketing sessions. When we applied supervised neural networks to create financial models, we measured the value added by measuring the performance (return), the risks, and the portfolio turnover of the models; we compared results with those of benchmarks (e.g. performance of human traders, or models based on more traditional methods).Return is usually compared to risk to obtain the risk-adjusted return. This risk-adjusted return can be compared to a benchmark (e.g. the risk-adjusted return of the Standard and Poor 500). By adding portfolio turnover one can take into account the costs of trading. The higher the turnover the higher the transaction costs. The tradeoff between risk-adjusted return and costs then provides a measure of effectiveness of trading models.The quality of an unsupervised neural net model can and should be measured on the basis of (i) the number of clusters; ii) the quality of clustering; (iii) the stability of clustering (as measured by the similarity or lack of similarity obtained by varying the testing data set). If we assess unsupervised neural net models in this way we are likely to find that there are many tradeoffs between quantity, quality, and stability of the clusters. It is then be up to the user to determine what is the best combination in the light of the objectives of the study. Some applications may demand maximum data reduction (minimum number of clusters), and can live with coarse map quality and low stability; other applications may demand refined maps (i.e. sharp differences between clusters), good stability, but do not require a lot of data reduction. For example, in macro-economic analyses, analyses of world development indicators, environmental conditions, analyses of global poverty and the like, maximum data reduction may be most desired because the maps would be mainly used for policy formulation and macro decision-support. In other applications such as mapping opportunities for options and future trading, fund manager selection, client segmentation, product differentiation, or market analyses, much finer differentiation between clusters may be desired.There is a vast domain of research and innovation to be done in this area, in particular in developing standards and a standard method for measuring the value added of clustering using self-organizing maps in financial, economic and marketing applications.* Guido Deboeck is an expert on advanced technology and its applications for financial engineering and management In the past twenty years he has been a leading innovator and advisor on technology to the World Bank in Washington. He holds an MA and Ph.D. degrees in Economics from Clark University. E-mail:gdeboeck@ ReferencesGuido Deboeck & Teuvo Kohonen : Visual Explorations in Finance with self-organizing maps, Springer-Verlag, 1998, 250 pp.Guido Deboeck : Trading on the Edge: Neural, Genetic and Fuzzy Systems for Chaotic Financial Markets, John Wiley and Sons, New York, April 1994, 377 pp.Teuvo Kohonen: Self-Organizing Map, Springer Verlag. 2nd edition, 1997, 426 pp.。