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A Framework for Mining Weighted Association Rule Using Hits Progress: Fuzzy Approach

R. Lokesh Kumar, Dr. P. Sengottuvelan


Data mining is to extract useful information from a vast amount of data, typically a large database. Association rule mining is a key issue in data mining, which follows link analysis technique. The goal of this technique is to detect relationships or associations between specific values of categorical variables in large data sets. This is a common task in many data mining projects, however the classical models ignore the difference between the transactions, and the weighted association rule mining does not work on databases with only binary attributes. It takes the quality of transactions into consideration using link-based models. W-support can be worked out without much overhead, and interesting patterns may be discovered through this new measurement. Next WARM is discussed then the evaluation of transactions with HITS, followed by the definition of w-support and the Apriori mining algorithm. In this paper, a new measure w-support, which does not require preassigned weights, can be used to work on databases with only binary attributes.


Association Rule Mining, Fuzzy Mining, HITS, WARM.

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