Cluster Algorithms

This category contains 11 nodes.

CLOPE (3.6) 

Yiling Yang, Xudong Guan, Jinyuan You: CLOPE: a fast and effective clustering algorithm for transactional data. In: Proceedings of the eighth ACM SIGKDD […]

Cobweb (3.6) 

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

DBScan (3.6) 

Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: […]

EM (3.6) 

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

FarthestFirst (3.6) 

Cluster data using the FarthestFirst algorithm. For more information see: Hochbaum, Shmoys (1985). A best possible heuristic for the k-center problem. […]

FilteredClusterer (3.6) 

Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter. Like the clusterer, the structure of the filter is based […]

MakeDensityBasedClusterer (3.6) 

Class for wrapping a Clusterer to make it return a distribution and density. Fits normal distributions and discrete distributions within each cluster […]

OPTICS (3.6) 

Mihael Ankerst, Markus M. Breunig, Hans-Peter Kriegel, Joerg Sander: OPTICS: Ordering Points To Identify the Clustering Structure. In: ACM SIGMOD […]

sIB (3.6) 

Cluster data using the sequential information bottleneck algorithm. Note: only hard clustering scheme is supported. sIB assign for each instance the […]

SimpleKMeans (3.6) 

Cluster data using the k means algorithm. Can use either the Euclidean distance (default) or the Manhattan distance. If the Manhattan distance is used, then […]