Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


March Edition 2023

International Journal of Applied Information Systems solicits high quality original research papers for the March 2023 Edition of the journal. The last date of research paper submission is February 15, 2023.

The Weekly Mining of Fuzzy Patterns from Temporal Datasets

Md Husamuddin

International Journal of Applied Information Systems
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Md Husamuddin
Download full text
  1. Md Husamuddin. The Weekly Mining of Fuzzy Patterns from Temporal Datasets. International Journal of Applied Information Systems 11(8):20-24, January 2017. URL, DOI BibTeX

    	author = "Md Husamuddin",
    	title = "The Weekly Mining of Fuzzy Patterns from Temporal Datasets",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "January 2017",
    	volume = 11,
    	number = 8,
    	month = "Jan",
    	year = 2017,
    	issn = "2249-0868",
    	pages = "20-24",
    	numpages = 5,
    	url = "",
    	doi = "10.5120/ijais2017451638",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"


The process of extracting fuzzy patterns from temporal datasets is a well known data mining problem. Weekly pattern is one such example where it reflects a pattern with some fuzzy time interval every week. This process involves two steps. Firstly, it finds frequent sets and secondly, it finds the association rules that occur in certain time intervals weekly. Most of the fuzzy patterns are concentrated as user defined. However, the probability of user not having prior knowledge of datasets being used in some applications is more. Thus, resulting in the loss of fuzziness related to the problem. The limitation of the natural language also bounds the user in specifying the same. This paper, proposes a method of extracting patterns that occur weekly in a particular fuzzy time frame and the fuzzy time frame is generated by the method itself. The efficacy of the method is backed by the experimental results.


  1. 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.
  2. J. M. Ale, and G. H. Rossi; An Approach to Discovering Temporal Association Rules, In Proc. of 2000 ACM symposium on Applied Computing (2000).
  3. 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.
  4. A. K. Mahanta, F. A. Mazarbhuiya, and H. K. Baruah (2008). Finding Calendar-based Periodic Patterns, Pattern Recognition Letters, Vol. 29(9), Elsevier publication, USA, pp. 1274-1284.
  5. H. K. Baruah (2010); The Randomness-Fuzziness consistency principle, International Journal of Energy, Information and Communications, Vol 1(1), Nov 2010, Japan.
  6. F. A. Mazarbhuiya (2014); Discovering Yearly Fuzzy Patterns, International Journal of Computer Science and Information security (IJCSIS) Vol. 12, No. 9, September 2014.
  7. M. Shenify and F. A. Mazarbhuiya (2015); Discovering Monthly Fuzzy Patterns, International Journal of Intelligence Science (IJIS), 37-43, USA.
  8. F. A. Mazarbhuiya (2015); Extracting daily fuzzy patterns, International Journal of Computer Science and Information security (IJCSIS) Vol. 44, No. 1, January 2016 .
  9. F. A. Mazarbhuiya and Yusuf Perwej (2015); Mining Hourly Fuzzy Patterns from Temporal Datasets, International Journal of Engineering Research and Technology (IJERT), vol 4, issue 10, October 2015.
  10. C. M. Antunes, and A. L. Oliviera; Temporal Data Mining an overview, Workshop on Temporal Data Mining-7th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, (2001).
  11. 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.
  12. Y. Li, P. Ning, X. S. Wang, and S. Jajodia; Discovering Calendar-based Temporal Association Rules, Elsevier Science, (2001).
  13. G. Zimbrado, J. Moreira de Souza, V. Teixeira de Almeida, and W. Arauja de Silva; An Algorithm to Discover Calendar-based Temporal Association Rules with Item’s Lifespan Restriction, In Proc. of the 8th ACM SIGKDD 2002.
  14. R.B.V. Subramanyam, A. Goswami, Bhanu Prasad; Mining fuzzy temporal patterns from process instances with weighted temporal graphsInt. J. of Data Analysis Techniques and Strategies, 2008 Vol.1, No.1, pp.60 – 77.
  15. S. Jain, S. Jain, and A. Jain; An assessment of Fuzzy Temporal Rule Mining, International Journal of Application or Innovation in Engineering and Management (IJAIEM), Vol. 2, 1, January 2013, pp. 42-45.
  16. Wan-Ju Lee, Jung-Yi Jiang and Shie-Jue Lee; Mining fuzzy periodic association rules, Data & Knowledge Engineering, Vol. 65, Issue 3, June 2008, pp. 442-462.
  17. Klir, J. and Yuan, B.; Fuzzy Sets and Logic Theory and Application, Prentice Hill Pvt. Ltd. (2002).
  18. H. K. Baruah; Set Superimposition and its application to the Theory of Fuzzy Sets, Journal of Assam Science Society, Vol. 10 No. 1 and 2, (1999), pp. 25-31.
  19. D. Dubois and H. Prade; Ranking fuzzy numbers in the setting of possibility theory, Inf. Sc.30, (1983), pp. 183-224.


Temporal Patterns, Temporal Association rules, Superimposed intervals, Fuzzy set, Right reference functions, left reference functions, Membership functions