Frequent Pattern Mining Based On Clustering and Association Rule Algorithm
Main Article Content
Abstract
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.
Â
Keywords: Data mining, K-Medoids, Most Frequent pattern mining, clustering, association
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.