LAUSR

The process of extracting patterns that are frequent from supermarket datasets is a well known problem of data mining. Nowadays, we have many approaches to resolve the problem. Association rule mining is one among them. Supermarket data are usually temporal in nature as they record all the transactions in the supermarket, with the time of occurrence. An algorithm has been proposed to find frequent itemsets, taking the temporal attributes in supermarket dataset. The best part of the algorithm is that each frequent itemset extracted by it is associated with a list of time intervals in which it is frequent. Taking time of transactions as calendar dates, we may get various types of periodic patterns viz. yearly, quarterly, monthly, etc. If the time intervals associated with a periodic itemset are kept in a compact manner, it turns out to be a fuzzy time interval. Clustering of such patterns can be a useful data mining problem. In this paper, we put forward an agglomerative hierarchical clustering algorithm which is able to extracts clusters among such periodic itemsets. Here we take two similarity measures, one on the itemsets of the clusters and others on the corresponding fuzzy time intervals. The efficiency of the proposed method is demonstrated through experimentation on real datasets.