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