【原创】数据挖掘房价预测英文课件演示PPT 图文
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数据挖掘全英文课件
Ratio
temperature in Kelvin, monetary quantities, counts, age, mass, length, electrical current
Attribute Level
Transformation
Comments
Nominal
Any permutation of values
calendar dates, temperature in Celsius or Fahrenheit
mean, standard deviation, Pearson's correlation, t and F tests geometric mean, harmonic mean, percent variation
Ordinal
An order preserving change of values, i.e., new_value = f(old_value) where f is a monotonic function.
Interval
new_value =a * old_value + b where a and b are constants
Single Married Single Married
– Attribute is also known as variable, field, characteristic, or feature Objects
Divorced 95K Married 60K
A collection of attributes describe an object
– Object is also known as record, point, case, sample, entity, or instance
数据挖掘PPT01Intro
Data Mining:
Concepts and Techniques
(3rd ed.)
— Chapter 1 —
Jiawei Han, Micheline Kamber, and Jian Pei
University of Illinois at Urbana-Champaign & Simon Fraser University
Alternative names
Watch out: Is everything ―data mining‖?
(Deductive) expert systems
7
Knowledge Discovery (KDD) Process
This is a view from typical database systems and data Pattern Evaluation warehousing communities Data mining plays an essential role in the knowledge discovery Data Mining process Task-relevant Data Data Warehouse Data Cleaning Data Integration Databases
© 2011 Han, Kamber & Pei. All rights reserved.
1
Chapter 1. Introduction
Why Data Mining?
What Is Data Mining?
A Multi-Dimensional View of Data Mining What Kind of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Technology Are Used? What Kind of Applications Are Targeted? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society
Concepts and Techniques
(3rd ed.)
— Chapter 1 —
Jiawei Han, Micheline Kamber, and Jian Pei
University of Illinois at Urbana-Champaign & Simon Fraser University
Alternative names
Watch out: Is everything ―data mining‖?
(Deductive) expert systems
7
Knowledge Discovery (KDD) Process
This is a view from typical database systems and data Pattern Evaluation warehousing communities Data mining plays an essential role in the knowledge discovery Data Mining process Task-relevant Data Data Warehouse Data Cleaning Data Integration Databases
© 2011 Han, Kamber & Pei. All rights reserved.
1
Chapter 1. Introduction
Why Data Mining?
What Is Data Mining?
A Multi-Dimensional View of Data Mining What Kind of Data Can Be Mined? What Kinds of Patterns Can Be Mined? What Technology Are Used? What Kind of Applications Are Targeted? Major Issues in Data Mining A Brief History of Data Mining and Data Mining Society
《数据挖掘》PPT课件
➢ 数据挖掘应用系统开发 ➢ 数据挖掘技术的新应用 ➢ 数据挖掘软件发展
2020/12/9
数据库研究所
9
高级数据挖掘
课程的教学目的
➢ 让学生掌握数据挖掘的基本概念、算法和高级技术； ➢ 将这些概念、算法和技术应用于实际问题。
复旦大学计算机科学技术学 院基本情况
➢ 主要研究方向
▪ 媒体计算 ▪ 数据库与数据科学 ▪ 网络与信息安全 ▪ 智能信息处理 ▪ 人机接口和服务计算 ▪ 理论计算机科学 ▪ 软件工程与系统软件
2020/12/9
数据库研究所
6
复旦大学数据挖掘课程的设置
总体目标
➢ 掌握大规模数据挖掘与分析的基本流程 ➢ 掌握数据挖掘的基本算法 ➢ 掌握对实际数据集进行挖掘的系统能力
数据仓库与数据挖掘
数据库系统
2020/12/9
数据库研究所
8
数据仓库与数据挖掘
课程的教学目的
➢ 掌握数据仓库数据挖掘原理、技术和方法，掌握建立数据挖掘应用 系统的方法，了解相关前沿的研究。
教学内容
➢ 数据挖掘、数据仓库的基本概念
▪ 数据仓库设计和应用 ▪ 数据挖掘的基本技术
• 关联分析、分类分析、聚类分析、异常分析和演化分析等；联机分析处理OLAP技术；
➢ involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.
➢ The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
2020/12/9
数据库研究所
9
高级数据挖掘
课程的教学目的
➢ 让学生掌握数据挖掘的基本概念、算法和高级技术； ➢ 将这些概念、算法和技术应用于实际问题。
复旦大学计算机科学技术学 院基本情况
➢ 主要研究方向
▪ 媒体计算 ▪ 数据库与数据科学 ▪ 网络与信息安全 ▪ 智能信息处理 ▪ 人机接口和服务计算 ▪ 理论计算机科学 ▪ 软件工程与系统软件
2020/12/9
数据库研究所
6
复旦大学数据挖掘课程的设置
总体目标
➢ 掌握大规模数据挖掘与分析的基本流程 ➢ 掌握数据挖掘的基本算法 ➢ 掌握对实际数据集进行挖掘的系统能力
数据仓库与数据挖掘
数据库系统
2020/12/9
数据库研究所
8
数据仓库与数据挖掘
课程的教学目的
➢ 掌握数据仓库数据挖掘原理、技术和方法，掌握建立数据挖掘应用 系统的方法，了解相关前沿的研究。
教学内容
➢ 数据挖掘、数据仓库的基本概念
▪ 数据仓库设计和应用 ▪ 数据挖掘的基本技术
• 关联分析、分类分析、聚类分析、异常分析和演化分析等；联机分析处理OLAP技术；
➢ involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.
➢ The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.
房价问题英文PPT
But how to do with China's housing problem ?
I made a promise to the Chinese people last year that I would try to keep housing prices at a reasonable level during my tenure, and I won't shrink from the goal . —— 温家宝
On the other hand , the supply of real estate has been insufficient（不足） due to the extremely strict land control ever since April 2004 . In the circumstance（环境） of supply shortage , high-end demand was met first .
China’s Housing Price Top 10
1 2 3 4 5 杭州 25840 北京 22310 上海 19168 温州 18854 三亚 18319 6 7 8 6 舟山 10500
你懂的，不解释！ You know that and I
The government
【B】 Limiting speculative behavior by regulating tax 【C】 Controlling population growth
【A】 Delivering more affordable housing （经济适用房）
Us , ordinary citizens
China's Housing Problem （Ben-C-Halls）
《数据挖掘》之分类和预测PPT(37张)
G(A a ) I( is 1 ,n s 2 ,.s m .) .E ,(A )
具有高信息增益的属性，是给定集合中具有高区分度 的属性。所以可以通过计算S中样本的每个属性的信 息增益，来得到一个属性的相关性的排序。
age youth youth middle_aged senior senior senior middle_aged youth youth senior youth middle_aged middle_aged senior
buys_computer = “yes” IF age = “senior” AND credit_rating = “fair” THEN buys_computer =
“no”
可伸缩性与决策归纳树
分类挖掘是一个在统计学和机器学习的领域也 被广为研究的问题，并提出了很多算法，但是 这些算法都是内存驻留的
分类和预测
分类 VS. 预测
分类和预测是两种数据分析形式，用于提取描 述重要数据类或预测未来的数据趋势 的模型
分类：
预测类对象的分类标号（或离散值） 根据训练数据集和类标号属性，构建模型来分类现有数据
，并用来分类新数据
预测：
建立连续函数值模型 比如预测空缺值，或者预测顾客在计算机设备上的花费
4. 对测试属性每个已知的值，创建一个分支， 并以此划分元组
5. 算法使用同样的过程，递归的形成每个划分 上的元组决策树。一旦一个属性出现在一个 节点上，就不在该节点的任何子节点上出现
6. 递归划分步骤停止的条件
划分D（在N节点提供）的所有元组属于同一类 没有剩余属性可以用来进一步划分元组——使用多数表决 没有剩余的样本 给定分支没有元组，则以D中多数类创建一个树叶
最新数据挖掘应用PPT课件
ESL recommender teaching and learning
Right/wrong answer statistical table
For every student, the system creates a right/wrong answer statistical table: a wrong answer is represented by 1 and a right answer by 0.
The semantic-expansion approach that integrates semantic information for spreading expansion and content-based filtering for document recommendation.
Inadequate information in IR
One possible solution for overcoming the problem is to expand the query by adding more semantic information to better describe the concepts. Relevance feedbacks and knowledge structure are used to add appropriate terms to expand the queries.
Customer lifetime value analysis is defined as the prediction of the total net income a company can expect from a customer. Up/Cross selling refers to promotion activities which aim at augmenting the number of associated or closely related services that a customer uses within a firm.
数据挖掘：概念与技术完整(英文)3prepppt课件
.
6
Chapter 3: Data Preprocessing
Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary
Simon Fraser University, Canada
http://www.cs.sfu.ca
30.05.2020
.
1
Chapter 3: ta Preprocessing
Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary
noisy: containing errors or outliers
inconsistent: containing discrepancies in codes or names
No quality data, no quality mining results!
Quality decisions must be based on quality data
30.05.2020
.
7
Data Cleaning
Data cleaning tasks Fill in missing values Identify outliers and smooth out noisy data Correct inconsistent data
人工智能与数据挖掘教学课件-2.datawarehouse
Subject-Oriented
The Data Warehouse data is arranged and optimized to provide answers to questions coming from diverse functional areas within a company.
What is Data Warehouse
The idea of a data warehouse is to put a wide range of operational data from internal and external sources into one place so it can be better utilized by executives, line of business managers and other business analysts.
The Data Warehouse
Time Variant
The Warehouse data represent the flow of data through time. It can even contain projected data.
Non-Volatile
Once data enter the Data Warehouse, they are never removed.
The Data Warehouse
The Data Warehouse is an integrated, subject-oriented, time-variant, nonvolatile database that provides support for decision making.
The Data Warehouse data is arranged and optimized to provide answers to questions coming from diverse functional areas within a company.
What is Data Warehouse
The idea of a data warehouse is to put a wide range of operational data from internal and external sources into one place so it can be better utilized by executives, line of business managers and other business analysts.
The Data Warehouse
Time Variant
The Warehouse data represent the flow of data through time. It can even contain projected data.
Non-Volatile
Once data enter the Data Warehouse, they are never removed.
The Data Warehouse
The Data Warehouse is an integrated, subject-oriented, time-variant, nonvolatile database that provides support for decision making.
房价 英文 PPT
Loans for the Dream
Feverish Property Market
• Possessing a house is everyone’s dream. For most home buyers, they not only have the actual house buying to deal with, but they also have the mortgage process to encounter. • A great demand for houses causes considerable sum of loans from banks. Economists have cautioned the central government of growing risks posed by the real estate bubbles, which if bursts, might trigger the American-style mortgage crisis, dealing a blow to the country’s banking system.
Script
After a series of administrative policies regarding the property market, housing prices in Beijing have continued to skyrocket. Here is a second-home seller in the capital's central business district. His apartment is 155 square meters. "Now I'm selling my apartment for 6.2 million yuan, not 5.9 million, and I won't bargain." The Ministry of Land and Resources says the real estate market is foaming. For the first time, the government introduced the concept of a return rate in its 2009 land price report. The return rate is the number of annual apartment rentals divided by property price. When the rate is above 5.5 percent, housing prices have room to increase. But if it is below 4.5 percent, local housing prices have existing bubbles. Take the return rate of apartments in Beijing's central business district for instance. It is only 0.7 percent, indicating a huge price bubble. Property agencies in Beijing say property owners are constantly asking for higher prices for their apartments and refusing to bargain. Zhao Fangwei is a property agency manager. "Beginning in March, property owners have been calling us every day and asking us to update their posted prices. On average, they ask for one thousand yuan more per square meter every day. The property ad displays hanging outside can hardly represent the changing speed of prices."
Feverish Property Market
• Possessing a house is everyone’s dream. For most home buyers, they not only have the actual house buying to deal with, but they also have the mortgage process to encounter. • A great demand for houses causes considerable sum of loans from banks. Economists have cautioned the central government of growing risks posed by the real estate bubbles, which if bursts, might trigger the American-style mortgage crisis, dealing a blow to the country’s banking system.
Script
After a series of administrative policies regarding the property market, housing prices in Beijing have continued to skyrocket. Here is a second-home seller in the capital's central business district. His apartment is 155 square meters. "Now I'm selling my apartment for 6.2 million yuan, not 5.9 million, and I won't bargain." The Ministry of Land and Resources says the real estate market is foaming. For the first time, the government introduced the concept of a return rate in its 2009 land price report. The return rate is the number of annual apartment rentals divided by property price. When the rate is above 5.5 percent, housing prices have room to increase. But if it is below 4.5 percent, local housing prices have existing bubbles. Take the return rate of apartments in Beijing's central business district for instance. It is only 0.7 percent, indicating a huge price bubble. Property agencies in Beijing say property owners are constantly asking for higher prices for their apartments and refusing to bargain. Zhao Fangwei is a property agency manager. "Beginning in March, property owners have been calling us every day and asking us to update their posted prices. On average, they ask for one thousand yuan more per square meter every day. The property ad displays hanging outside can hardly represent the changing speed of prices."
数据挖掘第五章ppt
24
解析特征化:一个例子
任务 使用解析特征化挖掘Big-University研究生的一般特 征描述 给定 属性:name, gender, major, birth_place, birth_date, phone#, and gpa Gen(ai) = ai上的概念层 Ui = ai属性解析阈值 Ti = ai 的属性归纳阈值 R = 属性相关阈值
用来分类一个对象的最小测试数量
See example 2006年11月17日星期五 Data Mining: Concepts and Techniques
22
判定树自定向下归纳
属性= {Outlook, Temperature, Humidity, Wind} 打网球 = {yes, no}
Outlook sunny Humidity high no
2006年11月17日星期五
Data Mining: Concepts and Techniques
7
面向属性的归纳
1989年首次提出 不局限于分类数据也不局限于特定的度量. 它是怎麽做的? 使用关系数据库查询收集任务相关数据 通过属性删除和属性概化进行概化 通过合并相等的广义元组，并累计它们对应的 计数值来进行聚集 和用户的交互式表示
2006年11月17日星期五 Data Mining: Concepts and Techniques
2
什么是概念描述?
描述式数据挖掘和预测式数据挖掘 描述式数据挖掘: 以简洁、概要的方式描述概念 和任务相关的数据集 预测式数据挖掘：在数据和分析的基础上，为数 据库构造模型并预测未知数据的趋势和属性 概念描述: 特征化: 提供给定数据汇集的简洁汇总 比较: 提供两个或多个数据汇集的比较描述
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Description
• Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. • With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
Description
• Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. • With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
The Big Assignment of Data Mining
by Rongsheng Zhu 2017-12-3
Topic One
• House Prices: Advanced Regression Techniques • Predict sales prices and practice feature engineering, RFs, and gradient boosting
The Big Assignment of Data Mining
by Rongsheng Zhu 2017-12-3
Topic One
• House Prices: Advanced Regression Techniques • Predict sales prices and practice feature engineering, RFs, and gradient boosting
The Big Assignment of Data Mining
by Rongsheng Zhu 2017-12-3
Topicห้องสมุดไป่ตู้One
• House Prices: Advanced Regression Techniques • Predict sales prices and practice feature engineering, RFs, and gradient boosting
Description
• Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. • With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
The Big Assignment of Data Mining
by Rongsheng Zhu 2017-12-3
Topic One
• House Prices: Advanced Regression Techniques • Predict sales prices and practice feature engineering, RFs, and gradient boosting
Description
• Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence. • With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.