global-journal-of-computer-science-and-technology-c-software-data-engineering Global Journal of Computer Science and Technology - C: Software & Data Engineering 0975-4350 0975-4172 Global Journals Publishing Group Incorporated 74413 Data Mining Based on Semantic Similarity to mine new Association Rules MahajanAakanksha JainDr. Sandeep INDIA, Doon Valley Institute of Engineering And Technology 15 01 2012 12 C12 11 17

The problem 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 milk; 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  Milk. 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.

 Data mining Semantic similarity Association Rules Support Confidence Fuzzy logic. Full Text

The problem 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 milk; 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  Milk. 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.