Algorithms for clustering data
Algorithms for clustering data
Introduction to data structures and algorithms related to information retrieval
Information retrieval
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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ISPD '98 Proceedings of the 1998 international symposium on Physical design
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SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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Journal of Parallel and Distributed Computing
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Journal of the ACM (JACM)
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SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
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IEEE Transactions on Knowledge and Data Engineering
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
An implementation of web image search engines
ICADL'04 Proceedings of the 7th international Conference on Digital Libraries: international collaboration and cross-fertilization
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Journal of Visual Communication and Image Representation
'Oh web image, where art thou?'
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Semantic analysis and retrieval in personal and social photo collections
Multimedia Tools and Applications
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This paper provides a novel Web image clustering methodology based on their associated texts. In our approach, the semantics of Web images are firstly represented into vectors of term-weight pairs. In order to correctly correlate terms to a Web image, the associated text of the Web image is partitioned into semantic blocks according to the semantic structure of the text with respect to the Web images. The weight of a term in the vector of an embedded Web image is calculated with respect to both its local occurrence in semantic blocks and the distances of the blocks to the image. With this method, ‘Web image clustering’ is transformed into ‘term vector clustering’. And a feature based solution is employed in our solution. To reach this objective, we define the associate relations between two terms based on their co-occurrence in the associated text of the Web images. Thus, a term semantic network (TSN) is constructed with terms as the nodes and associate relationships as the edges. To cluster terms in TSN, CHAMELEON algorithm is utilized. In order to determine the significances of terms in each cluster, HITS algorithm is applied. Finally, web images are assigned to different clusters based on the similarity between image term vectors and the term vector of the clusters.