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Cluster Algorithms

This category contains 15 nodes.

CascadeSimpleKMeans (3.7)

Cascade simple k means, selects the best k according to calinski-harabasz criterion

CLOPE (3.7)

Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data

Cobweb (3.7)

Class implementing the Cobweb and Classit clustering algorithms. Note: the application of node operators (merging, splitting etc.) in terms of ordering and […]

DBSCAN (3.7)

Basic implementation of DBSCAN clustering algorithm that should *not* be used as a reference for runtime benchmarks: more sophisticated implementations […]

EM (3.7)

Simple EM (expectation maximisation) class. EM assigns a probability distribution to each instance which indicates the probability of it belonging to each […]

FarthestFirst (3.7)

Cluster data using the FarthestFirst algorithm. For more information see: Hochbaum, Shmoys (1985)

FilteredClusterer (3.7)

Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter

HierarchicalClusterer (3.7)

Hierarchical clustering class. Implements a number of classic agglomorative (i.e

LVQ (3.7)

A Clusterer that implements Learning Vector Quantization algorithm for unsupervised clustering

MakeDensityBasedClusterer (3.7)

Class for wrapping a Clusterer to make it return a distribution and density