Combining statistics and semantics via ensemble model for document clustering

ABSTRACT

Incorporating background knowledge into data mining algorithms is an important but challenging problem. Current approaches in semi-supervised learning require explicit knowledge provided by domain experts, knowledge specific to the particular data set. In this study, we propose an ensemble model that couples two sources of information: statistics information that is derived from the data set, and sense information retrieved from WordNet that is used to build a semantic binary model. We evaluated the efficacy of using our combined ensemble model on the Reuters-21578 and 20newsgroups data sets.

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INDEX TERMS

Keywords:
WordNet, disambiguation, ensemble learning, text clustering