A New Algorithm for Frequent Itemset Generation in Non-Binary Search Space

Association rule induction is a powerful data mining method, used to analyze the regularities in data trends by finding the frequent itemset and association between items or set of items. Several research attempts have been done for the purpose based on the binary data space. In this paper an algorithm has been proposed for the same purpose but based on the non-binary data space. The algorithm is capable to generate the frequent itemset more close to the real life situations as it consider the strength of presence of each items implicitly. Also the algorithm can be directly applicable to the real time data repository for finding the frequent itemset.