An Efficient Implementation of an Algorithm for Mining Locally Frequent Patterns
Fokrul Alom Mazarbhuiya
Mining patterns from large dataset is an interested data mining problem. Many methods have been developed for this purpose till today. Most of the methods considered the time attributes as one of the normal attribute. However taking the time attribute into account separately the patterns can be extracted which cannot be extracted by normal methods. These patterns are termed as temporal patterns A couple of works have already been done in mining temporal patterns. A nice algorithm for mining locally frequent patterns from temporal datasets is proposed by Anjana et al. In this article, we propose a hash-tree based implementation of the algorithm. We also established the fact that the hash-tree based outperforms others.
Data Mining, Frequent patterns, Temporal patterns, Locally frequent patterns.
 R. Agrawal, T. Imielinski and A. N. Swami, Mining association rules between sets of items in large databases, In Proc. of 1993 ACM SIGMOD Int’l Conf on Management of Data, Vol. 22(2) of SIGMOD Records, ACM Press, (1993), pp 207-216.
 R. Agrawal and R. Srikant; Fast Algorithms for Mining Association Rules, In Proc. of the 20th VLDB Conf., Santiago, Chile, 1994.
 J. M. Ale and G. H. Rossi; An approach to discovering temporal association rules, In Proc. of 2000 ACM symposium on Applied Computing (2000).
 B. Ozden, S. Ramaswamy and A. Silberschatz; Cyclic Association Rules, In Proc. of the 14th Int’l Conf. on Data Engineering, USA (1998), pp. 412-421.
 A. K. Mahanta, F. A. Mazarbhuiya and H. K. Baruah; Finding Locally and Periodically Frequent Sets and Periodic Association Rules, In Proc. of 1st Int’l Conf. on Pattern Recognition and Machine Intelligence, LNCS 3776 (2005), pp. 576-582.
 A. K. Mahanta, F. A. Mazarbhuiya and H. K. Baruah, Finding calendar-based periodic patterns, Pattern Recognition Letters, vol.29, no.9, pp.1274-1284, 2008.
 Tom Brijs, G. Swinnen, K. Vanhoof and G. Wets; using association rules for product assortment decisions: A case study. In Knowledge Discovery and Data Mining (1999), pp.254-260.
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[Fokrul Alom Mazarbhuiya
(2016), An Efficient Implementation of an Algorithm for Mining Locally Frequent Patterns, International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-3, Issue-1, Page No-55-58], (ISSN 2347 - 5552). www.ijirem.org
Fokrul Alom Mazarbhuiya
College of Computer Science & IT, Albaha University, Albaha, KSA email@example.com