Package dragon.ir.clustering

A package for document clustering and its evaluation

See:
Description

Interface Summary

Clustering

Interface of text clustering method

Class Summary

AbstractClustering	Abstract class for document clustering
BasicKMean	Basic KMeans clustering
BisectKMean	BisectKMean clustering
ClusteringEva	Class for evaluating clustering results
DocCluster	Data structure for document cluster
DocClusterSet	Data structure for a set of document clusters.
HierClustering	Hierarchical clustering with options of single, complete, and average linkage
LinkKMean	Link-based K-Means Clustering Algorithm

Package dragon.ir.clustering Description

A package for document clustering and its evaluation

Package Specification

The toolkit implements two common clustering approaches, the agglomerative approach and the K-Means approach. These two approaches have many variants in terms of similarity measures. The toolkit encapsulates the details of different similarity measures into the implementations of two interfaces, Doc Distance and Cluster Model, respectively. The Doc Distance interface computes the distance between two documents and is designed for agglomerative clustering approaches. The Cluster Model interface computes the distance between a document and a cluster or the generative probability of a document by a cluster model. To evaluate cluster quality, please call dragon.ir.clustering.ClusteringEva.