clj-ml.clusterers
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
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-buildclj
(clusterer-build clusterer dataset)Applies a clustering algorithm to a set of data
Applies a clustering algorithm to a set of data
clusterer-clusterclj
(clusterer-cluster clusterer dataset)Add a class to each instance according to the provided clusterer
Add a class to each instance according to the provided clusterer
clusterer-evaluatecljmultimethod
Evaluates a trained clusterer using the provided dataset or cross-validation
Evaluates a trained clusterer using the provided dataset or cross-validation
clusterer-infocljmultimethod
Retrieves the data from a cluster, these data are clustering-algorithm dependent
Retrieves the data from a cluster, these data are clustering-algorithm dependent
clusterer-updateclj
(clusterer-update clusterer instance-s)If the clusterer is updateable it updates the cluster with the given instance or set of instances
If the clusterer is updateable it updates the cluster with the given instance or set of instances
make-clusterercljmultimethod
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
-
: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
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
* :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
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