Document clustering via adaptive subspace iteration

Document clustering has long been an important problem in information retrieval. In this paper, we present a new clustering algorithm ASI1, which uses explicitly modeling of the subspace structure associated with each cluster. ASI simultaneously performs data reduction and subspace identification via an iterative alternating optimization procedure. Motivated from the optimization procedure, we then provide a novel method to determine the number of clusters. We also discuss the connections of ASI with various existential clustering approaches. Finally, extensive experimental results on real data sets show the effectiveness of ASI algorithm. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Clustering; I.2 [Artificial Intelligence]: Learning; I.5 [Pattern Recognition]: Applications General Terms Algorithms, Experimentation, Measurement, Performance, Theory, Verification Keywords document clustering, adaptive subspace identification, alternating optimizatio...

• Type: Conference
• Year: 2004
• Where: SIGIR
• Authors: Tao Li, Sheng Ma, Mitsunori Ogihara
• Keywords: Document Clustering, Optimization Procedure, SIGIR 2004, Subspace Identification