89 lines
3.2 KiB
Plaintext
89 lines
3.2 KiB
Plaintext
fkmeans is a tiny C library that allows you to perform k-means clustering
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algorithm over arbitrary sets of n-dimensional data. All you need to do is:
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- Include the file kmeans.h in your sources;
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- Consider your data set as a vector of vectors of double items (double**),
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where each vector is an n-dimensional item of your data set;
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- If you want to perform the k-means algorithm over your data and you already
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know the number k of clusters there contained, or its estimate, you want to
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execute some code like this (in this example, the data set is 3-dimensional,
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i.e. it contains N vectors whose size is 3, and we know it contains n_clus
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clusters):
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kmeans_t *km;
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double **dataset;
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...
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km = kmeans_new ( dataset, N, 3, n_clus );
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kmeans ( km );
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...
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kmeans_free ( km );
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If you don't already know the number of clusters contained in your data set,
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you can use the function kmeans_auto() for automatically attempting to find
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the best one using Schwarz's criterion. Be careful, this operation can be very
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slow, especially if executed on data set having many elements. The example
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above would simply become something like:
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kmeans_t *km;
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double **dataset;
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...
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km = kmeans_auto ( dataset, N, 3 );
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...
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kmeans_free ( km );
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- Once the clustering has been performed, the clusters of data can be simply
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accessed from your kmeans_t* structure, as they are held as a double*** field
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named "clusters". Each vector in this structure represents a cluter, whose
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size is specified in the field cluster_sizes[i] of the structure. Each cluster
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contains the items that form it, each of it is an n-dimensional vector. The
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number of clusters is specified in the field "k" of the structure, the
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number of dimensions of each element is specified in the field "dataset_dim"
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and the number of elements in the originary data set is specified in the field
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"dataset_size". So, for example:
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for ( i=0; i < km->k; i++ )
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{
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printf ( "cluster %d: [ ", i );
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for ( j=0; j < km->cluster_sizes[i]; j++ )
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{
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printf ( "(" );
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for ( k=0; k < km->dataset_size; k++ )
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{
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printf ( "%f, ", km->clusters[i][j][k] );
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}
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printf ( "), ");
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}
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printf ( "]\n" );
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}
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The library however already comes with a sample implementation, contained in
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"test.c", and typing "make" this example will be built. This example takes 0,
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1, 2 or 3 command-line arguments, in format
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$ ./kmeans-test [num_elements] [min_value] [max_value]
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and randomly generates a 2-dimensional data set containing num_elements, whose
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coordinates are between min_value and max_value. The clustering is then
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performed and the results are shown on stdout, with the clusters coloured in
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different ways;
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- After you write your source, remember to include the file "kmeans.c",
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containing the implementation of the library, in the list of your sources
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files;
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- That's all. Include "kmeans.h", write your code using
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kmeans_new()+kmeans()+kmeans_free() or kmeans_auto()+kmeans_free(), explore
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your clusters, remember to include "kmeans.c" in the list of your source
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files, and you're ready for k-means clustering.
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Author: Fabio "BlackLight" Manganiello,
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<blacklight@autistici.org>,
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http://0x00.ath.cx
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