Improved Data Mining Based on Semantic Similarity to Mine New Association Rules

Abstract

The problems of mining association rules in a database are introduced. Most of association rule mining approaches aim to mine association rules considering exact matches between items in transactions. A new algorithm called ―Improved Data Mining Based on Semantic Similarity to mine new Association Rules‖ which considers not only exact matches between items, but also the semantic similarity between them. Improved Data Mining (IDM) Based on Semantic Similarity to mine new Association Rules uses the concepts of an expert to represent the similarity degree between items, and proposes a new way of obtaining support and confidence for the association rules containing these items. An association rule is for ex: i.e. for a grocery store say ―30% of transactions that contain bread also contain butter; 2% of all transactions contain both of these items‖. Here 30% is called the confidence of the rule, and 2% the support of the rule and this rule is represented as Bread  Butter. The problem is to find all association rules that satisfy user-specified minimum support and minimum confidence constraints. This paper then results that new rules bring more information about the database.