CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
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Knowledge workers are burdened with information overload. The information they need might be scattered in many places, buried in a file system, in their email, or on the web. Traditional Clustering algorithms help in assimilating these wide sources of information and generating meaningful relationships amongst them. A typical clustering preprocessing involves tokenization, removal of stop words, stemming, pruning etc. In this paper, we propose the use of summary and heuristics of a document as a pre-processing technique. This technique preserves the formatting of a document and uses this information for producing better clusters. In addition, only a summary of a document is used as the basis for clustering instead of the whole document. Clustering algorithms using the proposed pre-processing technique on formatted documents resulted in improved and more meaningful clusters.