CSC277 - Project 5
Clustering

Algorithm

Write a program to calculate the clusters for a given dataset, choosing one hierarchical clusting method, and one statistical clustering method.

Your input vector data will be in a standard data format, where each line will start with the name of the vector, followed by the data for that vector. All vectors will be the same size.

For example, the greedy algorithm discussed in class for K-means is as follows: 

kmeans(dataset, k):
    Read in data vectors
    Randomly select k data points to be centers of clusters
    While the clusters have changed composition:
    	Asign every data point to it's nearest cluster center (euclidean distance)
    	Recalculate the center for each cluster as the average of all points in that cluster

The goal of K-means is to minimize the squared error distortion, which for a set V of data points and X of centers, is defined as

for the center X closest to each vi.

Output

The output of your programs should be the clusters found and their membership, as well as the squared error distortion.

Testing

Test you above algorithms on the data found in the yeast_stress.pcl file provided.

What do you believe is the optimal number of clusters?

Turn in your code and output from testing in your csc277 directory on the cs.centenary.edu server in a project 5 folder.