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 descriptioncentric, state-of-the-art search results clusterin...

• Type: Conference
• Year: 2010
• Where: SIGIR
• Authors: Claudio Carpineto, Giovanni Romano
• Keywords: Information Technology, Meta, Meta Clustering, Meta Clustering Problem, SIGIR 2010