Examples of KMeansSeed

edu.ucla.sspace.clustering.seeding.KMeansSeed
An interface for KMeans seeding algorithms. Implementations must compose initial centroid seeds from a data set by either choosing an already existing data point or composing a linear combination of existing data points.
Implementations must be state free and threadsafe. @author Keith Stevens

Examples of edu.ucla.sspace.clustering.seeding.KMeansSeed

        // Get the number of repitetions to use.
        int numRepetitions = Integer.parseInt(properties.getProperty(
                    REPEAT_PROPERTY, DEFAULT_REPEATS));


        // Get an instance of the seed generator.
        KMeansSeed seedType = ReflectionUtil.getObjectInstance(
                properties.getProperty(SEED_PROPERTY, DEFAULT_SEED));


        // Create the criterion function.
        CriterionFunction criterion = ReflectionUtil.getObjectInstance(
                properties.getProperty(CRITERIA_PROPERTY, DEFAULT_CRITERION));

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