Weighted Association Rule Mining and Clustering in Non-binary Search Space

Association Rule Induction and Clustering are some of the most useful data mining techniques. The former focuses on finding regularities in data trends, while the latter on discovering groups and identifying interesting distributions and patterns in the underlying data. Several research attempts have been made for the purpose of clustering transactions 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. We also propose a novel mechanism to yield weighted association rules in non-binary search space. It is capable of clustering and mining weighted association rules more close to the real life situations as it considers the Strength of Presence of each item implicitly. Also they can be directly applied to the real time data repository.