Algorithms for clustering data
Algorithms for clustering data
Recent trends in hierarchic document clustering: a critical review
Information Processing and Management: an International Journal
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
The smart document retrieval project
SIGIR '91 Proceedings of the 14th annual international ACM SIGIR conference on Research and development in information retrieval
Inference networks for document retrieval
Inference networks for document retrieval
Constant interaction-time scatter/gather browsing of very large document collections
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Selective text utilization and text traversal
HYPERTEXT '93 Proceedings of the fifth ACM conference on Hypertext
Learning collection fusion strategies
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic hypertext construction
Automatic hypertext construction
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
The cluster hypothesis revisited
SIGIR '85 Proceedings of the 8th annual international ACM SIGIR conference on Research and development in information retrieval
On the hardness of approximating minimization problems
Journal of the ACM (JACM)
Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
SFCS '93 Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science
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We present and analyze the star clustering algorithm. We discuss an implementation of this algorithm that supports browsing and document retrieval through information organization. We define three parameters for evaluating a clustering algorithm to measure the topic separation and topic aggregation achieved by the algorithm. In the absence of benchmarks, we present a method for randomly generating clustering data. Data from our user study shows evidence that the star algorithm is effective for organizing information.