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Return to Session Vb: Clustering
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.
@inproceedings{1009031, author = {Tao Li and Sheng Ma and Mitsunori Ogihara}, title = {Document clustering via adaptive subspace iteration}, booktitle = {SIGIR '04: Proceedings of the 27th annual international conference on Research and development in information retrieval}, year = {2004}, isbn = {1-58113-881-4}, pages = {218--225}, location = {Sheffield, United Kingdom}, doi = {http://doi.acm.org/10.1145/1008992.1009031}, publisher = {ACM Press}, }  ©2005 Association for Computing Machinery |
