Abstract
Data mining, also known as Knowledge Discovery in Databases (KDD), includes the task to find anomalies, correlations, patterns, and trends to predict outcomes [1, 2]. Association rule mining is one of the most prominent data mining tasks along with classification and clustering is gaining much importance in recent years many application domains. In general, the KDD is a sequence of processes stated [3] as follows: • Data cleaning which includes the removal of noise and inconsistency from the data. • Data integration, where multiple data sources are combined and integrated into one. • Data selection, where data relevant to analysis task are retrieved. • Data transformation, in which data is transformed into forms appropriate for mining by performing several aggregation operations. • Data mining, which includes intelligent methods to extract various patterns in data. • Pattern evaluation, where the various patterns are evaluated and the ones truly representing the knowledge are identified. • Knowledge representation, including techniques to represent the knowledge.
