数据仓库与数据挖掘考试

Data Mining Exam Manual
By Ronel Li, Zhangwei Tuo, Jia Li, Info dept
I. fill in the blanks (20%)
1. From a data analysis point of view, data mining can be classified into two categories: ( predictive ) and ( descriptive ) data mining.
2. Data model in data warehouse contains conceptual model, ( logical model ),( physical ) model ) and metadata model.
3. From user view, metadata can be divided into ( business ) and ( technical ) metadata.
4. OLAP operations in the multidimensional data model are ( slice ), dice ( drill-down ), ( roll-up ) and rotate.
5. Methods of data preprocessing include（data cleaning, data integration & transformation, data reduction ）
6. In data warehouse, Data can be organized ( current detailed ), older detailed, ( lightly summarized ) and highly summarized.
7. A three-tier data warehousing architecture include a warehouse database server ( OLAP server ) and client.
8. ( Data discretization ) techniques can be used to reduce the number of values for a given continuous attributes, by dividing the range of the attribute into intervals.
9. ( A concept hierarchy ) defines a sequence of mappings from a set of low-level concepts to higher-level, more general concepts.
10. ( A data cube ) allows data to be modeled and viewed in multiple dimensions, it is defined by dimensions and facts.
11. In general, major clustering methods can be classified into the following categories: partitioning methods, hierarchical methods, ( density-based methods ) ( grid-based methods ) and model-based methods.
II.True-false question (30%)
Please try your own luck …
III.Short Answer Questions (20%)
1.How the noisy data is smoothed with binning during preprocessing?
Answer:
Binning methods smooth a sorted data value by consulting the values around it.
Binning Techniques includes:
1)Smoothing by bin means,
2)Smoothing by bin medians,
3)Smoothing by bin boundries.
2.The theory or basic steps of k-means algorithm.
Answer:
Following is the theory structure of K-means algorithm:
arbitrarily choose k objects from D as the initial cluster centers;
repeat
(re)assign each object to the cluster to which the object is the most similar, based on the mean value of the objects in the cluster;
update the cluster means, i.e., calculate the mean value of the objects for each cluster;
until no change.
3.How to fill the empty property values?
Answer:
The methods of filling the empty property values are below:
1)Ignore the tuple
2)Fill in the missing value manually
3)Use a global constant to fill in the missing va lue
4)Use the attribute mean to fill in the missing value
5)Use the attributemean for all samples belonging to the same class as the given tuple
6)Use the most probable value to fill in the missing value
IV.Calculation (20%) (Simple Instructions Here)
1.Given minimum support factor, How to find the frequent item sets of association rules.
English textbook page: 233
Chinese textbook page: 148
2. Calculate the value of a property's information gain using the decision tree algorithm (A specific dataset would be given).
Gain(A)=Info(D)-Info A (D)
Info(D)=()i m i i D D
21
log ∑=
Info A (D)=)(||||1j v j j D
Info D D ⨯∑=
English textbook page: 192
Chinese textbook page: 287
3. How to measure the dissimilarity of two objects in the cluster? How to calculate the distance of binary attributes d(i, j) = t
s r q s r ++++ symmetric binary dissimilarity. d(i, j) =
s r q s r +++ asymmetric binary dissimilarity.
English textbook page: 256
Chinese textbook page: 343。