Association Rule Mining

Data Mining is the discovery of hidden information found in databases and can be viewed as a step in the knowledge discovery process. Data mining functions include clustering, classification, prediction, and link analysis (associations). One of the most important data mining applications is that of mining association rules. An association rule has two parts, an antecedent (if) and a consequent (then). An antecedent is an item found in the data. A consequent is an item that is found in combination with the antecedent. Association Rule Mining: Overview Association rules are created by analyzing data for frequent if/then patterns and using the criteria support and confidence to identify the most important relationships. Support is an indication of how frequently the items appear in the database. Confidence indicates the number of times the if/then statements have been found to be true. Association rule mining has been an active research area in data mining, for which many algorithms have been developed. In data mining, association rule learning is a popular and well-accepted method for discovering interesting relations between variables in large databases. Association rules are employed today in many areas including web usage mining, intrusion detection and bioinformatics. In general, the association rule…

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