SSDM: A Semantically Similar Data Mining Algorithm

Most of association rule mining approaches aim to mine association rules considering exact matches between items in transactions. In this paper we present a new algorithm called SSDM (Semantically Similar Data Miner), which considers not only exact matches between items, but also the semantic similarity between them. SSDM uses fuzzy logic concepts to represent the similarity degree between items, and proposes a new way of obtaining support and confidence for the association rules containing these items.