Book chapter
Latent Semantic Space for Web Clustering
Data Mining: Foundations and Practice
2008
Abstract
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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Details
- Title
- Latent Semantic Space for Web Clustering
- Creators
- I. Jen Chiang - National Taiwan UniversityTsau Young (‘T. Y.’) Lin - Department of Computer Science, San Jose State University, San Jose, USAHsiang-Chun Tsai - National Taiwan UniversityJau-Min Wong - National Taiwan UniversityXiaohua Hu - Drexel University
- Publication Details
- Data Mining: Foundations and Practice
- Series
- Studies in Computational Intelligence
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-51349145194
- Other Identifier
- 991019173515904721