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- W2140942285 abstract "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." @default.
- W2140942285 created "2016-06-24" @default.
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- W2140942285 date "2004-07-25" @default.
- W2140942285 modified "2023-10-16" @default.
- W2140942285 title "Document clustering via adaptive subspace iteration" @default.
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- W2140942285 doi "https://doi.org/10.1145/1008992.1009031" @default.
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