Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Optimization of inverted vector searches
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
ACM Computing Surveys (CSUR)
Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Information Retrieval
Document clustering based on maximal frequent sequences
FinTAL'06 Proceedings of the 5th international conference on Advances in Natural Language Processing
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems: CoopIS, COA, and ODBASE - Volume Part II
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The general goal of clustering is to group data elements such that the intra-group similarities are high and the inter-group similarities are low. We present a clustering algorithm called CBC (Clustering By Committee) that is shown to produce higher quality clusters in document clustering tasks as compared to several well known clustering algorithms. It initially discovers a set of tight clusters (high intra-group similarity), called committees, that are well scattered in the similarity space (low inter-group similarity). The union of the committees is but a subset of all elements. The algorithm proceeds by assigning elements to their most similar committee. Evaluating cluster quality has always been a difficult task. We present a new evaluation methodology based on the editing distance between output clusters and manually constructed classes (the answer key). This evaluation measure is more intuitive and easier to interpret than previous evaluation measures.