Frequent Pattern Mining Based On Clustering and Association Rule Algorithm

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Kavita M. Gawande
Mr. Subhash K.Shinde, Mrs .Dipti Patil


Decision making is considered as one of the most difficult tasks in restaurants as food items are perishable. Managers always want to analyze summaries of sales, to get aware of customer preferences, to figure out which items or combinations of items should be put on sale or to simply acquire various kinds of marketing information. To fulfil this need, this paper is aimed to provide customer’s buying patterns of food items using data mining techniques. Analysis of sales data shows that some food items are sold frequently while some food products are sold rarely. This paper proposes a method that groups the food items as slow selling, medium-selling and fast selling items using KMedoids clustering algorithm. These clusters intern are given as input for the association rule mining based Apriori algorithm and Most Frequent Pattern Mining algorithm to generate frequent patterns. Experimental results show that the proposed method generates useful patterns which may assist manager in decision making. The algorithm is evaluated by using standard dataset and is compared with the results of other algorithms considering computational time and other parameters as quality measures.


Keywords: Data mining, K-Medoids, Most Frequent pattern mining, clustering, association


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