optimal meta search results clustering
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.
optimization search results clustering meta clustering