TITLE:
A Novel Approach for Clustering Periodic Patterns
AUTHORS:
Fokrul Alom Mazarbhuiya
KEYWORDS:
Pattern Mining, Temporal Patterns, Locally Frequent Patterns, Superimposition of Intervals, Fuzzy Time-Interval
JOURNAL NAME:
International Journal of Intelligence Science,
Vol.7 No.1,
December
30,
2016
ABSTRACT: 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.