Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Simple and Fast: Improving a Branch-And-Bound Algorithm for Maximum Clique
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
An efficient branch-and-bound algorithm for finding a maximum clique
DMTCS'03 Proceedings of the 4th international conference on Discrete mathematics and theoretical computer science
An overview of web data clustering practices
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Finding significant web pages with lower ranks by pseudo-clique search
DS'05 Proceedings of the 8th international conference on Discovery Science
Finding Top-N Pseudo Formal Concepts with Core Intents
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
An extended branch and bound search algorithm for finding top-N formal concepts of documents
JSAI'06 Proceedings of the 20th annual conference on New frontiers in artificial intelligence
An efficient algorithm for enumerating pseudo cliques
ISAAC'07 Proceedings of the 18th international conference on Algorithms and computation
An algorithm for extracting rare concepts with concise intents
ICFCA'10 Proceedings of the 8th international conference on Formal Concept Analysis
User community reconstruction using sampled microblogging data
Proceedings of the 21st international conference companion on World Wide Web
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This paper presents a method for Pinpoint Clustering of web pages. We try to find useful clusters of web pages which are significant in the sense that their contents are similar to ones of higher-ranked pages. Since we are usually careless of lower-ranked pages, they are unconditionally discarded even if their contents are similar to some pages with high ranks. Such hidden pages together with significant higher-ranked pages are extracted as a cluster. As the result, our clusters can provide new valuable information for users. In order to obtain such clusters, we first extract semantic correlations among terms by applying Singular Value Decomposition (SVD) to the term-document matrix generated from a corpus. Based on the correlations, we can evaluate potential similarities among web pages to be clustered. The set of web pages is represented as a weighted graph G based on the similarities and their ranks. Our clusters can be found as pseudo-cliques in G. An algorithm for finding Top-N weighted pseudo-cliques is presented. Our experimental result shows that a quite valuable cluster can be actually extracted according to our method. We also discuss an idea for improvement on meanings of clusters. With the help of Formal Concept Analysis, our clusters, called FC-based clusters, can be provided with clear meanings. Our preliminary experimentation shows that the extended method would be a promising approach to finding meaningful clusters.