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Co-clustering documents and words using Bipartite Spectral Graph Partitioning
, 2001
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A methodology for clustering XML documents by structure
- Information Systems
, 2006
"... The processing and management of XML data are popular research issues. However, operations based on the structure of XML data have not received strong attention. These operations involve, among others, the grouping of structurally similar XML documents. Such grouping results from the application of ..."
Abstract
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Cited by 50 (0 self)
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The processing and management of XML data are popular research issues. However, operations based on the structure of XML data have not received strong attention. These operations involve, among others, the grouping of structurally similar XML documents. Such grouping results from the application of clustering methods with distances that estimate the similarity between tree structures. This paper presents a framework for clustering XML documents by structure. Modeling the XML documents as rooted ordered labeled trees, we study the usage of structural distance metrics in hierarchical clustering algorithms to detect groups of structurally similar XML documents. We suggest the usage of structural summaries for trees to improve the performance of the distance calculation and at the same time to maintain or even improve its quality. Our approach is tested using a prototype testbed.
Information Organization Algorithms
, 2000
"... In this paper we present a system for static and dynamic information organization and show our evaluations of this system on TREC data. We introduce the off-line and on-line star clustering algorithms for information organization. Our evaluation experiments show that the off-line star algorithm outp ..."
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In this paper we present a system for static and dynamic information organization and show our evaluations of this system on TREC data. We introduce the off-line and on-line star clustering algorithms for information organization. Our evaluation experiments show that the off-line star algorithm outperforms the single link and average link clustering algorithms. Since the star algorithm is also highly efficient and simple to implement, we advocate its use for tasks that require clustering, such as information organization, browsing, filtering, routing, topic tracking, and new topic detection.
