TY - CHAP
T1 - Latent semantic space for web clustering
AU - Chiang, I-Jen
AU - Lin, Tsau Young
AU - Tsai, Hsiang Chun
AU - Wong, Jau Min
AU - Hu, Xiaohua
PY - 2008
Y1 - 2008
N2 - To organize a huge amount of Web pages into topics, according to their relevance, is the efficient and effective method for information retrieval. Latent Semantic Space (LSS) naturally in the form on some geometric structure in Combinatorial Topology has been proposed for unstructured document clustering. Given a set of Web pages, the set of associations among frequently co-occurring terms in them forms naturally a CONCEPT, which is represented as a set of connected components of the simplicial complexes. Based on these concepts, Web pages can be clustered into meaningful categories.
AB - To organize a huge amount of Web pages into topics, according to their relevance, is the efficient and effective method for information retrieval. Latent Semantic Space (LSS) naturally in the form on some geometric structure in Combinatorial Topology has been proposed for unstructured document clustering. Given a set of Web pages, the set of associations among frequently co-occurring terms in them forms naturally a CONCEPT, which is represented as a set of connected components of the simplicial complexes. Based on these concepts, Web pages can be clustered into meaningful categories.
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U2 - 10.1007/978-3-540-78488-3_4
DO - 10.1007/978-3-540-78488-3_4
M3 - Chapter
AN - SCOPUS:51349145194
SN - 9783540784876
T3 - Studies in Computational Intelligence
SP - 61
EP - 77
BT - Data Mining
ER -