Rough clustering of sequential data
Data & Knowledge Engineering
From Information System to Decision Support System
Transactions on Rough Sets IX
Web Snippet Clustering Based on Text Enrichment with Concept Hierarchy
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Inducing word senses to improve web search result clustering
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Autonomous knowledge-oriented clustering using decision-theoretic rough set theory
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Clustering web search results with maximum spanning trees
AI*IA'11 Proceedings of the 12th international conference on Artificial intelligence around man and beyond
Semantic Web search based on rough sets and Fuzzy Formal Concept Analysis
Knowledge-Based Systems
Enhancing search result clustering with semantic indexing
Proceedings of the Third Symposium on Information and Communication Technology
Autonomous Knowledge-oriented Clustering Using Decision-Theoretic Rough Set Theory
Fundamenta Informaticae - Rough Sets and Knowledge Technology (RSKT 2010)
An automatic method to determine the number of clusters using decision-theoretic rough set
International Journal of Approximate Reasoning
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Due to the enormous size of the web and low precision of user queries, finding the right information from the web can be difficult if not impossible. One approach that tries to solve this problem is using clustering techniques for grouping similar document together in order to facilitate presentation of results in more compact form and enable thematic browsing of the results set. The main problem of many web search result (snippet) clustering algorithm is based on the poor vector representation of snippets. In this paper, we present a method of snippet representation enrichment using Tolerance Rough Set model. We applied the proposed method to construct a rough set based search result clustering algorithm and compared it with other recent methods.