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Document Type

Original Study

Abstract

Data mining is the task of discovering interesting knowledge from large amounts of data where the data can be stored in database, data warehouse, or other information repositories. A knowledge discovery process includes data cleaning, data integration, data selection, data transformation, data mining, pattern eVolution, and knowledge presentation. Data mining is a good way for extracting or mining knowledge from amount of data for classification, predication, estimation, clustering or association rules or any activities, which need decision. Association rules identify relationships between attributes in a database. Association rule mining consists of first finding frequent itemsets which satisfy a minimum support threshold, and then computes confidence percentage for each k-itemsets to construct strong association rules. The proposed algorithm aims to produce association rules depending on logical AND operation by converting the database transaction into binary representation and neglecting any sum (column) less than threshold to find the identical column in (k-1)-itemset table with the column in k-itemset table which represent the association rules.

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