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The S-Space Package is a collection of algorithms for building Semantic Spaces as well as a highly-scalable library for designing new distributional semantics algorithms. Distributional algorithms process text corpora and represent the semantic for words as high dimensional feature vectors. This package also includes matrices, vectors, and numerous clustering algorithms. These approaches are known by many names, such as word spaces, semantic spaces, or distributed semantics and rest upon the Distributional Hypothesis: words that appear in similar contexts have similar meanings.

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/*
 * Copyright 2009 Keith Stevens 
 *
 * This file is part of the S-Space package and is covered under the terms and
 * conditions therein.
 *
 * The S-Space package is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License version 2 as published
 * by the Free Software Foundation and distributed hereunder to you.
 *
 * THIS SOFTWARE IS PROVIDED "AS IS" AND NO REPRESENTATIONS OR WARRANTIES,
 * EXPRESS OR IMPLIED ARE MADE.  BY WAY OF EXAMPLE, BUT NOT LIMITATION, WE MAKE
 * NO REPRESENTATIONS OR WARRANTIES OF MERCHANT- ABILITY OR FITNESS FOR ANY
 * PARTICULAR PURPOSE OR THAT THE USE OF THE LICENSED SOFTWARE OR DOCUMENTATION
 * WILL NOT INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR OTHER
 * RIGHTS.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program. If not, see .
 */

package edu.ucla.sspace.clustering;

import edu.ucla.sspace.matrix.Matrix;

import java.util.Properties;


/**
 * An interface for any Ofline clustering implementation.  This interface
 * supports hard and soft clustering by returning a {@link Assignment}.  A
 * {@link Matrix} is passed in where each row is to be considered the set of
 * data points to cluster.
 *
 * 
 *
 * Implementations should not modify the contents of a {@code Matrix} to
 * cluster.  Implementations may use a passed in properties object to support
 * additional parameters that for clustering.  By convention, implementations
 * should specify a set of default values such that the matrix rows can be
 * clustered if no properties are specified. 
 *
 * 
 *
 * If a clustering algorithm requires the number of clusters to be specified, an
 * implementation may throw an {@link UnsupportedOperationException} when the
 * number of clusters is not specified.
 *
 * @author Keith Stevens
 */
public interface Clustering {

    /**
     * Clusters the set of rows in the given {@code Matrix} without a specified
     * number of clusters (optional operation).  The set of cluster assignments
     * are returned for each row in the matrix.
     *
     * @param matrix the {@link Matrix} whose row data points are to be
     *        clustered
     * @param props the properties to use for any parameters each clustering
     *        algorithm may need 
     *
     * @return an array of {@link Assignment} instances that indicate zero or
     *         more clusters to which each row belongs.
     */
    Assignments cluster(Matrix matrix, Properties props);

    /**
     * Clusters the set of rows in the given {@code Matrix} into the specified
     * number of clusters.  The set of cluster assignments are returned for each
     * row in the matrix.
     *
     * @param matrix the {@link Matrix} whose row data points are to be
     *        clustered 
     * @param numClusters the number of clusters to generate
     * @param props the properties to use for any parameters each clustering
     *        algorithm may need 
     *
     * @return an array of {@link Assignment} instances that indicate zero or
     *         more clusters to which each row belongs.
     */
    Assignments cluster(Matrix matrix, int numClusters, Properties props);
}