Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Clustering transactions using large items
Proceedings of the eighth international conference on Information and knowledge management
ACM Computing Surveys (CSUR)
Frequent term-based text clustering
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Ontologies Improve Text Document Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Incremental Clustering and Dynamic Information Retrieval
SIAM Journal on Computing
Multi-way distributional clustering via pairwise interactions
ICML '05 Proceedings of the 22nd international conference on Machine learning
Supervised clustering with support vector machines
ICML '05 Proceedings of the 22nd international conference on Machine learning
Aggregated cross-media news visualization and personalization
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Research of fast SOM clustering for text information
Expert Systems with Applications: An International Journal
Aggregated search: A new information retrieval paradigm
ACM Computing Surveys (CSUR)
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Clustering text documents is a basic enabling technique in a wide variety of Information and Knowledge Management applications. This paper presents an incremental clustering system to organize and manage Newsgroup articles. It serves administrators and readers of a Newsgroup to archive important postings and to get a structured over-view on current developments and topics. To be practically applicable, such a system must fulfill two conditions. First, it must be able to process rapidly changing text streams, modifying the cluster structure dynamically by adding, deleting and restructuring clusters. Second, it must consider the user in the incremental process. Severe changes in the organization structure are unacceptable for most users, even if they are optimal from the point of view of an abstract clustering criterion. We propose an approach to model the cost to accommodate to changes in the cluster structure explicitly. Users then may constraint, which changes are acceptable to them.