Clustering and Classification in Text Collections Using Graph Modularity

TL;DR

The paper introduces a fast algorithm for clustering and classifying large text collections using bipartite graph modularity, achieving high quality results and record-breaking speed.

Contribution

It presents a novel algorithm that leverages bipartite graph modularity for efficient text clustering and classification, outperforming existing methods in speed.

Findings

Competitive clustering and classification quality.

Record-breaking processing speed.

Effective use of bipartite graph modularity.

Abstract

A new fast algorithm for clustering and classification of large collections of text documents is introduced. The new algorithm employs the bipartite graph that realizes the word-document matrix of the collection. Namely, the modularity of the bipartite graph is used as the optimization functional. Experiments performed with the new algorithm on a number of text collections had shown a competitive quality of the clustering (classification), and a record-breaking speed.