Information Retrieval
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
A Concept-Driven Algorithm for Clustering Search Results
IEEE Intelligent Systems
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Introduction to Information Retrieval
Introduction to Information Retrieval
A survey of Web clustering engines
ACM Computing Surveys (CSUR)
Mobile information retrieval with search results clustering: Prototypes and evaluations
Journal of the American Society for Information Science and Technology
Full-Subtopic Retrieval with Keyphrase-Based Search Results Clustering
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Beyond precision@10: clustering the long tail of web search results
Proceedings of the 20th ACM international conference on Information and knowledge management
Topical clustering of search results
Proceedings of the fifth ACM international conference on Web search and data mining
Evaluating subtopic retrieval methods: Clustering versus diversification of search results
Information Processing and Management: an International Journal
A novel concept-based search for the web of data using UMBEL and a fuzzy retrieval model
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Increasing stability of result organization for session search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
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By analogy with merging documents rankings, the outputs from multiple search results clustering algorithms can be combined into a single output. In this paper we study the feasibility of meta search results clustering, which has unique features compared to the general meta clustering problem. After showing that the combination of multiple search results clusterings is empirically justified, we cast meta clustering as an optimization problem of an objective function measuring the probabilistic concordance between the clustering combination and the single clusterings. We then show, using an easily computable upper bound on such a function, that a simple stochastic optimization algorithm delivers reasonable approximations of the optimal value very efficiently, and we also provide a method for labeling the generated clusters with the most agreed upon cluster labels. Optimal meta clustering with meta labeling is applied to three description-centric, state-of-the-art search results clustering algorithms. The performance improvement is demonstrated through a range of evaluation techniques (i.e., internal, classification-oriented, and information retrieval-oriented), using suitable test collections of search results with document-level relevance judgments per subtopic.