SSDM: A Semantically Similar Data Mining Algorithm
نویسندگان
چکیده
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.
منابع مشابه
Using Fuzzy Ontologies to Extend Semantically Similar Data Mining
Association rule mining approaches traditionally generate rules based only on database contents, and focus on exact matches between items in transactions. In many applications, however, the utilization of some background knowledge, such as ontologies, can enhance the discovery process and generate semantically richer rules. Besides, fuzzy logic concepts can be applied on ontologies to quantify ...
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