clj-ml.clusterers — com.shark8me/clj-ml 0.8.9

This namespace contains several functions for building clusterers using different clustering algorithms. K-means, Cobweb and Expectation maximization algorithms are currently supported.

Some of these algorithms support incremental building of the clustering without having the full data set in main memory. Functions for evaluating the clusterer as well as for clustering new instances are also supported

clusterer-build clj

(clusterer-build clusterer dataset)

Applies a clustering algorithm to a set of data

clusterer-cluster clj

(clusterer-cluster clusterer dataset)

Add a class to each instance according to the provided clusterer

clusterer-evaluate clj multimethod

Evaluates a trained clusterer using the provided dataset or cross-validation

clusterer-info clj multimethod

Retrieves the data from a cluster, these data are clustering-algorithm dependent

clusterer-update clj

(clusterer-update clusterer instance-s)

If the clusterer is updateable it updates the cluster with the given instance or set of instances

make-clusterer clj multimethod

Creates a new clusterer for the given kind algorithm and options.

The first argument identifies the kind of clusterer. The second argument is a map of parameters particular to each clusterer.

The clusterers currently supported are:

• :k-means
• :cobweb
• :expectation-maximization

This is the description of the supported clusterers and the parameters accepted by each clusterer algorithm:

• :k-means

  A clusterer that uses the simple K-Means algorithm to build the clusters

  Parameters:

  • :display-standard-deviation Display the standard deviation of the centroids in the output for the clusterer. Sample value: true
  • :replace-missing-values Replaces the missing values with the mean/mode. Sample value: true
  • :number-clusters The number of clusters to be built. Sample value: 3
  • :random-seed Seed for the random number generator. Sample value: 0.3
  • :number-iterations Maximum number of iterations that the algorithm will run. Sample value: 1000
  • :initialization-method Initialization method to use. 0 = random, 1 = k-means++, 2 = canopy, 3 = farthest first. (default = 0)

• :cobweb

  Implementation of the Cobweb incremental algorithm for herarchical conceptual clustering.

  Parameters:

  • :acuity Acuity. Default value: 1.0
  • :cutoff Cutoff. Default value: 0.002
  • :random-seed Seed for the random number generator. Default value: 42.

• :expectation-maximization

  Implementation of the probabilistic clusterer algorithm for expectation maximization.

  Parameters:

  • :number-clusters Number of clusters to be built. If ommitted or -1 is passed as a value, cross-validation will be used to select the number of clusters. Sample value: 3
  • :maximum-iterations Maximum number of iterations the algorithm will run. Default value: 100
  • :minimum-standard-deviation Minimum allowable standard deviation for normal density computation. Default value: 1e-6
  • :random-seed Seed for the random number generator. Default value: 100

make-clusterer-m clj macro

(make-clusterer-m kind clusterer-class options)