Package smile.clustering

Clustering analysis.

See: Description

Interface Summary

Interface	Description
Clustering<T>	Clustering interface.

Class Summary

Class	Description
BBDTree	Balanced Box-Decomposition Tree.
BIRCH	Balanced Iterative Reducing and Clustering using Hierarchies.
BIRCHTest
CLARANS<T>	Clustering Large Applications based upon RANdomized Search.
CLARANSTest
DBScan<T>	Density-Based Spatial Clustering of Applications with Noise.
DBScanTest
DENCLUE	DENsity CLUstering.
DENCLUETest
DeterministicAnnealing	Deterministic annealing clustering.
DeterministicAnnealingTest
GMeans	G-Means clustering algorithm, an extended K-Means which tries to automatically determine the number of clusters by normality test.
GMeansTest
HierarchicalClustering	Agglomerative Hierarchical Clustering.
HierarchicalClusteringTest
KMeans	K-Means clustering.
KMeansTest
MEC<T>	Nonparametric Minimum Conditional Entropy Clustering.
MECTest
PartitionClustering<T>	Abstract class of partition clustering.
SIB	The Sequential Information Bottleneck algorithm.
SIBTest
SpectralClustering	Spectral Clustering.
SpectralClusteringTest
XMeans	X-Means clustering algorithm, an extended K-Means which tries to automatically determine the number of clusters based on BIC scores.
XMeansTest

Enum Summary

Enum	Description
ClusteringDistance	Clustering distance measure.

Package smile.clustering Description

Clustering analysis. Clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields.

Hierarchical algorithms find successive clusters using previously established clusters. These algorithms usually are either agglomerative ("bottom-up") or divisive ("top-down"). Agglomerative algorithms begin with each element as a separate cluster and merge them into successively larger clusters. Divisive algorithms begin with the whole set and proceed to divide it into successively smaller clusters.

Partitional algorithms typically determine all clusters at once, but can also be used as divisive algorithms in the hierarchical clustering. Many partitional clustering algorithms require the specification of the number of clusters to produce in the input data set, prior to execution of the algorithm. Barring knowledge of the proper value beforehand, the appropriate value must be determined, a problem on its own for which a number of techniques have been developed.

Density-based clustering algorithms are devised to discover arbitrary-shaped clusters. In this approach, a cluster is regarded as a region in which the density of data objects exceeds a threshold.

Subspace clustering methods look for clusters that can only be seen in a particular projection (subspace, manifold) of the data. These methods thus can ignore irrelevant attributes. The general problem is also known as Correlation clustering while the special case of axis-parallel subspaces is also known as two-way clustering, co-clustering or biclustering in bioinformatics: in these methods not only the objects are clustered but also the features of the objects, i.e., if the data is represented in a data matrix, the rows and columns are clustered simultaneously. They usually do not however work with arbitrary feature combinations as in general subspace methods.