Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Learning spatially variant dissimilarity (SVaD) measures
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning spatially variant dissimilarity (SVaD) measures
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning a Mahalanobis distance metric for data clustering and classification
Pattern Recognition
An active learning framework for semi-supervised document clustering with language modeling
Data & Knowledge Engineering
Mining evolutionary multi-branch trees from text streams
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Absolute and relative clustering
Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering
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Semi-supervised clustering algorithms partition a given data set using limited supervision from the user. In this paper, we propose a clustering algorithmthat uses supervision in terms of relative comparisons, viz., is closer to than to . The success of a clustering algorithm also depends on the kind of dissimilarity measure. The proposed clustering algorithm learns the underlying dissimilarity measure while finding compact clusters in the given data set. Through our experimental studies on high-dimensional textual data sets, we demonstrate that the proposed algorithm achieves higher accuracy than the algorithms using pairwise constraints for supervision.