data mining习题

附件三：课程部分习题

附件三：课程部分习题

1. The following table consists of training data from an employee database. The data have been generalized. For a given row entry, count represents the number of data tuples having the values for department, status, age, and salary given in that row.

Let status be the class label attributes.

(a) How would you modify the ID3 algorithm to take into consideration the count of each generalized data tuple (i.e., of each row entry)?

(b) Use your modified version of ID3 to construct a decision tree from the given data.

2. A database has 4 transactions. Let min-support =60%, min_confidence=80%. Find the longest frequent itemset(s). List all association rules that satisfy the above requirement, with supports and confidence.

TID Data Items bought

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T100 10/15/99 K, A, D, B

T200 10/15/99 D, A, C, E, B

T300 10/19/99 C, A, B, E

T400 10/22/99 B, A, D

3. Perform the third iteration of the k-means algorithm for the example given in the section “An Example Using K-Means”. What are the new cluster centers?

4. Suppose that the data mining task is to cluster the following 8 points (with (x,y) representing location) into 3 clusters.

A1(2,10), A2(2,5), A3(8,4), B1(5,8), B2(7,5), B3(6,4), C1(1,2), C2(4,9)

The distance function is Manhattan distance. Suppose initially we assign A1, B1, and C1 as the center of each cluster, respectively. Use the k-means algorithm to show only:

(a) the three cluster centers after the first round execution;

(b) the final three clusters.

5. Consider the feed-forward network in Figure 8.1 with the associated connection weights shown in Table 8.1. Apply the input instance [0.5, 0.2, 1.0] to the feed-forward neural network. r=0.5, Tk = 0.65. Specifically,

(a) Compute the input to node i and j;

(b) Use the sigmoid function to compute the initial output of nodes i and j;

(c) Use the ouput values computed in part b to determine the input and putput

values for node k;

(d) Adjust all weights for one epoch

Other exercises see text book.