C4.5: programs for machine learning
C4.5: programs for machine learning
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Introduction to Algorithms
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Information Retrieval
Introduction to Information Retrieval
Parameter-Free Hierarchical Co-clustering by n-Ary Splits
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Hierarchical co-clustering based on entropy splitting
Proceedings of the 21st ACM international conference on Information and knowledge management
Query Recommendation for Improving Search Engine Results
International Journal of Information Retrieval Research
Hierarchical co-clustering: off-line and incremental approaches
Data Mining and Knowledge Discovery
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Recently query log mining is extensively used by web information systems. In this paper a new hierarchical co-clustering for queries and URLs of a search engine log is introduced. In this method, firstly we construct a bipartite graph for queries and visited URLs, and then to discover noiseless clusters, all queries and related URLs are projected in a reduced dimensional space by applying singular value decomposition. Finally, all queries and URLs are iteratively clustered for constructing hierarchical categorization. The method has been evaluated using a real world data set and shows promising results.