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C4.5: programs for machine learning
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HyPursuit: a hierarchical network search engine that exploits content-link hypertext clustering
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The anatomy of a large-scale hypertextual Web search engine
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OPTICS: ordering points to identify the clustering structure
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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
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DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
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WISE '01 Proceedings of the Second International Conference on Web Information Systems Engineering (WISE'01) Volume 1 - Volume 1
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Data Mining and Knowledge Discovery
Clustering web pages based on their structure
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Clustering documents into a web directory for bootstrapping a supervised classification
Data & Knowledge Engineering - Special issue: WIDM 2003
Indexed-based density biased sampling for clustering applications
Data & Knowledge Engineering
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
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Web page clustering: a hyperlink-based similarity and matrix-based hierarchical algorithms
APWeb'03 Proceedings of the 5th Asia-Pacific web conference on Web technologies and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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IEEE Transactions on Neural Networks
Hierarchical web-page clustering via in-page and cross-page link structures
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The rapid increase of information on the web makes it necessary to improve information management techniques. One of the most important techniques is clustering web data. In this paper, we propose a new 3-phase clustering method that finds dense units in a data set using density-based algorithms. The distances in the dense units are stored in order in structures such as a min heap. In the extraction stage, these distances are extracted one by one, and their effects on the clustering process are examined. Finally, in the combination stage, clustering is completed using improved versions of well-known single and average linkage methods. All steps of the methods are performed in O(nlogn) time complexity. The proposed methods have the benefit of low complexity, and experimental results show they generate clusters with high quality. Other experiments also show that they provide additional advantages, such as clustering by sampling.