GDClust: A Graph-Based Document Clustering Technique

This paper introduces a new technique of document clustering based on frequent senses. The proposed system, GDClust (Graph-Based Document Clustering) works with frequent senses rather than frequent keywords used in traditional text mining techniques. GDClust presents text documents as hierarchical document-graphs and utilizes an Apriori paradigm to find the frequent subgraphs, which reflect frequent senses. Discovered frequent subgraphs are then utilized to generate sense-based document clusters. We propose a novel multilevel Gaussian minimum support approach for candidate subgraph generation. GDClust utilizes English language ontology to construct document-graphs and exploits graph-based data mining technique for sense discovery and clustering. It is an automated system and requires minimal human interaction for the clustering purpose.

• Type: Conference
• Year: 2007
• Where: ICDM
• Authors: M. Shahriar Hossain, Rafal A. Angryk
• Keywords: Data Mining, Document Clustering, Frequent Senses, Frequent Subgraphs, ICDM 2007