Clustering of Locally Frequent Patterns over Fuzzy Temporal Datasets

International Journal of Computer Trends and Technology (IJCTT)          
© 2015 by IJCTT Journal
Volume-28 Number-3
Year of Publication : 2015
Authors : Md Husamuddin, Fokrul Alom Mazarbhuiya
DOI :  10.14445/22312803/IJCTT-V28P124


Md Husamuddin, Fokrul Alom Mazarbhuiya "Clustering of Locally Frequent Patterns over Fuzzy Temporal Datasets". International Journal of Computer Trends and Technology (IJCTT) V28(3):131-134, October 2015. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
The study of discovering frequent patterns in a dataset is a well defined data mining problem. There are many approaches to resolve this problem including. Clustering is one of the common data mining approaches which is used for discovering data distribution and patterns in a dataset. Many algorithms have been proposed for finding clusters among frequent patterns itemsets. clustering fuzzy temporal data is an extension of temporal data mining. Here we try to find clusters among frequent itemsets based on fuzzy intervals of frequencies. In this paper, we propose a agglomerative hierarchical clustering algorithm to find clusters among the frequent itemsets obtained from fuzzy temporal data. The efficacy of the proposed method is established through experimentation on real datasets.

[1] J. A. Hartigan (1975);Clustering Algorithms, John Wiley & Sons, New York, USA.
[2] R. Agrawal, T. Imielinski and A. N. Swami (1993), 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, pp 207- 216.
[3] J. M. Ale and G. H. Rossi (2000); An approach to discovering temporal association rules, In Proc. of 2000 ACM symposium on Applied Computing.
[4] A. K. Mahanta, F. A. Mazarbhuiya and H. K. Baruah (2008); Finding calendar-based periodic patterns, Pattern Recognition Letters, vol.29, no.9, pp.1274-1284.
[5] F. A Mazarbhuiya, M. Shenify and Mohammed Husamuddin (2014); Finding Local and Periodic Association Rules from Fuzzy Temporal Data, The 2014 International Conference on Advances in Big Data Analytics, USA.
[6] M. Shenify, (2015); Extracting Cyclic Frequents Sets from Fuzzy Temporal Data, In proc of the 30th International Conference on Computers and their Applications (CATA- 2015), USA.
[7] F. A Mazharbhuiya and Muhammad Abulaish (2012); Clustering periodic frequent patterns using fuzzy statistical parameters, International journal of innovative computing, Information and control, vol.8, no.3(B), pp.2113-2124.
[8] M. Dutta, A. K. Mahanta and M. Mazumder (2001); An algorithm for clustering of categorical data using concept of neighours, Proc. of the 1st National Workshop on Soft Data Mining and Intelligent Systems, Tezpur University, India, pp.103-105.
[9] L. A. Zadeh (1965); Fuzzy Sets, Information and Control Vol. 8, pp. 338-353.
[10] M. Dutta and A. K. Mahanta (2004); An Algorithm for clustering large categorical databases using a fuzzy set based approach, Proc of the 17th Australian joint Conf. on Artificial Intelligence, Cairns, Australia.
[11] R. Agrawal and R. Srikant (1994); Fast Algorithms for Mining Association Rules, In Proc. of the 20th VLDB Conf., Santiago, Chile, 1994.
[12] N. K. Sindhu and R. Kaur (2013); Clustering in data mining, International Journal of Computer Trends and Technology (IJCTT), Vol. 4 (4), pp.710-714.
[13] A. N. Sravya, and M. Nalini Sri (2013); A vovel approach of temporal data clustering via weighted clustering ensemble with different representation, International Journal of Computer Trends and Technology (IJCTT), Vol. 4 (4), pp. 624-629.
[14] P. P. Pradhan, D. Mishra, S. Mishra, and S. Shaw (2013); Artificial Bee based Optimized Fuzzy c-Means Clustering of Gene Expression Data, International Journal of Computer Trends and Technology (IJCTT), Vol. 4 (5), pp 1-5.
[15] V. V Srivalli, R. G. Kumar, J. Mungara (2013); Hierarchical Clustering With Multi view point Based Similarity Measure, International Journal of Computer Trends and Technology (IJCTT), Vol. 4 (5), pp. 1475-1480.
[16] C. Carlsson and R. Fuller (2001); On Possibilistic Mean Value and Variance of Fuzzy Numbers, Fuzzy Sets and Systems 122 (2001), pp. 315-326.

Data mining, Clustering, Temporal patterns, Locally frequent itemset, Set superimposition, Fuzzy time-interval.