CS685: Special Topics in Data Mining (Spring 2009)

Homework 2: Due Feb 19^th

Goal: This homework will reinforce the understanding of two basic clustering algorithms - k-means and hierarchical clustering algorithms. Through the homework, you will learn how to use k-means and hierarchical clustering and how to evaluate the clustering results including determining the number of clusters.

Description of the homework:

Your homework will again be an application of existing algorithms. Only the report needs to be submitted. Your report should contain answers for the following questions.

Algorithms:

Two algorithms you are supposed to work with include K-means and hierarchical clustering.

Both algorithms are available as functions in matlab. (You can use other software, such as R and Weka, as long as you do the same analysis)

1) K-means

2) Hierarchical Clustering

Datasets:

Synthetic Control Chart Time Series Data Set.

http://archive.ics.uci.edu/ml/datasets/Synthetic+Control+Chart+Time+Series

Please Read through the data description carefully to understand the particular application.

For this dataset, the following is the set of real clusters underlying the data. You will need to compare your clustering results with the real clusters.

1-100 Normal

101-200 Cyclic

201-300 Increasing trend

301-400 Decreasing trend

401-500 Upward shift

501-600 Downward shift

Questions to answer in the report:

1. Write a script to compare two sets of clustering results

Assume C1 and C2 are two sets of clustering results on the same set of data. C1 = (S1, S2, …, Sn) contains n clusters, where each S represents a cluster and C2 = (T1, T2, …, Tn) contains n clusters as well, where each T is a cluster.

In order to compute the similary between C1 and C2 , you will need to do the following.

sim = 0

For each S in C1,

sim = sim + the highest similarity between S and any cluster T in C2 measured by Jaccard Coefficient.

End

avgsim = sim / n;

2. Please follow the instructions in matlab for K-means clustering to do the following.

a) Write the script to do k-means clustering. Check out the following function.

---Kmeans

b) run the program 10 times with k = 6. Check how the clustering algorithm changes by checking out sumdistance. Please report your result.

c) Use k = 2, 4, 6, 8, 10 and check how the sumdistance changes. Please report your result.

d) Pick one of the results as the best (please argue why), and compare it with real clusters.

3. Please follow the instructions in matlab for hierarchical clustering to do the following

a) Write the script to do hierarchical clustering. The following functions might be useful

--load

--Pdist

--Linkage

--Squareform

--Dendrogram

--Cluster

b) Choose one of the existing dissimilarity measures to compute the dissimilarity matrix for hierarchical clustering. Please reason whether it is appropriate for the particular application and suggest another that might also work.

c) Run the hierarchical clustering on the datasets using single linkage, complete linkage and maximum linkage. Generate 6 clusters for each run and Compare the change of the clustering results.

d) Pick one of the results as the best (please argue why), and compare it with real clusters.

4. Compare the best (3(d)) of the hierarchical clustering results generated in (c) with the best generated k-means clustering when k=6 (2(d)).

5. Please include all your matlab scripts for submission.

6. Please report your impression with the clustering algorithms and your experience with the assignments.