Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Sequence modelling for sentence classification in a legal summarisation system
Proceedings of the 2005 ACM symposium on Applied computing
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An approach to clustering short text snippets is proposed, which can be used to cluster search results into a few relevant groups to help users quickly locate their interesting groups of results. Specifically, the collection of search result snippets is regarded as a similarity graph implicitly, in which each snippet is a vertex and each edge between the vertices is weighted by the similarity between the corresponding snippets. TermCut , the proposed clustering algorithm, is then applied to recursively bisect the similarity graph by selecting the current core term such that one cluster contains the term and the other does not. Experimental results show that the proposed algorithm improves the KMeans algorithm by about 0.3 on FScore criterion.