International Journal of Intelligence Science, 2017, 7, 1-8 http://www.scirp.org/journal/ijis ISSN Online: 2163-0356 ISSN Print: 2163-0283 DOI: 10.4236/ijis.2017.71001 December 30, 2016 A Novel Approach for Clustering Periodic Patterns Fokrul Alom Mazarbhuiya Department of IT, College of Computer Science & IT, Albaha University, Albaha, KSA Abstract The process of extracting patterns that are frequent from supermarket data- sets is a well known problem of data mining. Nowadays, we have many ap- proaches to resolve the problem. Association rule mining is one among them. Supermarket data are usually temporal in nature as they record all the trans- actions in the supermarket, with the time of occurrence. An algorithm has been proposed to find frequent itemsets, taking the temporal attributes in su- permarket 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 pe- riodic patterns viz. yearly, quarterly, monthly, etc. If the time intervals asso- ciated 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 cluster- ing 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 pro- posed method is demonstrated through experimentation on real datasets. Keywords Pattern Mining, Temporal Patterns, Locally Frequent Patterns, Superimposition of Intervals, Fuzzy Time-Interval 1. Introduction The most important data mining problems based on unsupervised learning ap- proach is Clustering and it is very useful for the extraction of data distribution and patterns in the datasets. The clustering process is used to discover both the dense and sparse regions in a dataset. The two main broad approaches are parti- How to cite this paper: Mazarbhuiya, F.A. (2017) A Novel Approach for Clustering Periodic Patterns. International Journal of Intelligence Science, 7, 1-8. http://dx.doi.org/10.4236/ijis.2017.71001 Received: October 25, 2016 Accepted: December 27, 2016 Published: December 30, 2016 Copyright © 2017 by author and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access