Document clustering via adaptive subspace iteration

Tao Li, Sheng Ma, Mitsunori Ogihara
2004 Proceedings of the 27th annual international conference on Research and development in information retrieval - SIGIR '04  
Document clustering has long been an important problem in information retrieval. In this paper, we present a new clustering algorithm ASI 1 , 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
more » ... SI with various existential clustering approaches. Finally, extensive experimental results on real data sets show the effectiveness of ASI algorithm.
doi:10.1145/1008992.1009031 dblp:conf/sigir/LiMO04 fatcat:5mvq4atlgvh25irqrqwixcrgti