Clustering Project You are asked to implement several clustering methods: 1. lloyd.py : Lloyd’s k-means. 2. kmeanspp.py : k-means++. 3. cmeans.py : fuzzy c-means. Notice that the fuzzy c-means algorithm should be implemented to produce hard clustering. This should be done by first computing soft clustering and then converting the soft clustering to hard clustering. Input1: One data file. The data is a comma separated matrix of size n × m. Here the data points are the rows, not the columns. Input2: k, the number of desired clusters. Input3: r, the number of random iterations. Output: A comma separated file containing n integer values. Each value is in the range 0 . . . k−1. What you need to submit • Source code and documentation for the python scripts. You must be available to demonstrate your program to the TAs. Time slots and additional instructions will be announced later. Deadline: TBA.
