Implementations of different k-means algorithms in C.

Using guide:

Installation

With having gcc compiler and Make installing the program should be as easy as writing make. An executable "clbin" will be created.

Flags

Help menu. Argument and its value must be separated by a space:
-h  - help menu
-f  - input data points file name
-o  - output file name
-ci - centers input file name
-a  - algorithm choice (lloyd, elkan, hamerly, macqueen, hartigan, closest)
-i  - initialization method choice (kpp, forgy, partition, furthest, firstn)
-m  - metric choice (euclidean, manhattan)
-k  - clusters count, -ci flag has a higher priority
-s  - random seed nr, otherwise uses current time as the seed. Used to confirm clustering results
-n  - iteration count (default 100)

Contains the following:

Choosing initial cluster centers:

1. k-means++
2. Forgy
3. Partition (assigns points to random cluster and then finds means of these assignments as centers)
4. Furthest first
5. firstn (chooses k first points from input file as initial cluster centers).

Clustering algorithms:

1. Lloyd
2. Elkan
3. Hamerly
4. MacQueen
5. Hartigan-Wong
6. Closest (just assigns points to closest centers and stops)

Supports following metrics

1. Euclidean
2. Manhattan

It should be rather easy to add more of them.

Input data format

1. First line consists of two integers: Point count and data dimensionality. Follows n rows with d doubles on each of them.

Output data formats

Outputs 2 files

1. File consisting of numbers to which cluster a point belongs to
2. File consisting of means' vectors. First row has an integer which shows how many means follow.