Mining Fuzzy Association Rules Using Various Algorithms: A Survey

O. Gireesha*, O. Obulesu**
* PG Scholar, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, India.
** Assistant Professor, Department of Information Technology, Sree Vidyanikethan Engineering College, Tirupati, India.
Periodicity:April - June'2016
DOI : https://doi.org/10.26634/jse.10.4.6059

Abstract

The discovery of Association Rules (AR) acquire an imperative rule in Data Mining, which tries to find correlation among the attributes in a database. Classical Association Rules are meant for Boolean data and they suffer from a sharp boundary problem in handling quantitative data. Thereby, Fuzzy Association Rules (FAR) with fuzzy minimum support and confidence is introduced. In Fuzzy Association Rule Mining (FARM), determining fuzzy sets, tuning membership functions and automatic design of fuzzy sets are prominent objectives. Hence, FARM can be viewed as a multi-objective optimization problem. In this paper, different algorithms for FARM are discussed with merits and demerits.

Keywords

Association Rules, Data Mining, Fuzzy Sets, Membership Functions.

How to Cite this Article?

Gireesha, O., and Obulesu, O. (2016). Mining Fuzzy Association Rules Using Various Algorithms: A Survey. i-manager’s Journal on Software Engineering, 10(4), 30-36. https://doi.org/10.26634/jse.10.4.6059

References

[1]. R. Srikant, and R. Agrawal, (1996). “Mining Quantitative Association Rules in Large Relational Tables”. ACMSIGMOD,International Conference on Management of Data, pp.1-12, Montreal, Canada.
[2]. Chan, C.C. Keith, and A.U. Wai-Ho, (1997). “Mining fuzzy association rules”. In: Proceedings of the Sixth International Conference on Information and Knowledge Management, ACM.
[3]. M. Kuok, A.W. C. Fu, and M. H. Wong, (1998). “Mining Fuzzy Association Rules in Databases”. ACM SIGMOD Record, Vol. 27, No. 1, pp. 41-46.
[4]. W. Au, and K. Chan, (1998). “An Effective Algorithm for Discovering Fuzzy Rules in Relational Databases”. Proceeding of the 1998 IEEE International Conference on Fuzzy Systems, pp. 1314-1319, Anchorage, Alaska.
[5]. T. P. Hong, C. S. Kuo, and C. S. Chi, (2001). “Trade-off between Computation Time and Number of Rules for Fuzzy Mining from Quantitative Data”. International Journal of Uncertainty, Fuzziness and Knowledge-Based System, Vol. 9, No. 5, pp. 587- 604.
[6]. K. Hirota, and W. Pedrycz, (1999). “Fuzzy Computing for Data Mining”. Proceedings of the IEEE, Vol. 87, No. 9, pp. 1575.
[7]. W. Zhang, “Mining Fuzzy Quantitative Association rules”. Proceeding of the 11th International Conference on Tools with A.I., pp. 99-102, Chicago, IL, USA.
[8]. J. Han, J. Pei, and Y. Yin, (2000). “Mining frequent patterns without candidate generation”. The 2000 ACM SIGMOD International Conference on Management of Data, Vol. 29, No. 2, pp. 1-12.
[9]. H. Ishibuchi, T. Nakashima, and T. Murata, (2001). “Fuzzy Data Mining: Effect of Fuzzy Discretization”. Proceeding of 2001 IEEE International Conference on Data Mining, pp.241- 248.
[10]. S. Mitra, S. Pal, and P. Mitra, (2002). “Data Mining in Soft Computing Framework: A Survery”. IEEE Transactions on Neural Networks, Vol. 13, No. 1.
[11]. G. Chen, and Q. Wei, (2002). “Fuzzy Association Rules and the Extended Mining Algorithm”. Information Sciences, Vol. 147, pp.201-228.
[12]. Y. C. Hu, R. S. Chen, and G. H. Tzeng, (2003). “Discovering fuzzy Association Rules using Fuzzy Partition Methods”. Knowledge Based Systems, Vol. 16, pp. 137-147.
[13]. Tzung-Pei Hong, Chan - Sheng Kuo, and Shyue - Liang Wang, (2004). “A fuzzy AprioriTid mining algorithm with reduced computational time”. Applied Soft Computing, Vol. 5, No. 1, pp. 1-10.
[14]. S. Papadimitriou, and S. Mavroudi, (2005). “The fuzzy frequent pattern tree” In: The WSEAS International Conference on Computers, pp. 1-7.
[15]. E Ramaraj, K Rameshkumar, and N Venkatesan, (2008). “A better performed transaction reduction algorithm for mining frequent itemset from large voluminous database”. Proceedings of the 2nd National Conference, INDIACOM-2008, Vol. 5, pp. 1-10.
[16]. Reza Sheibani, and Amir Ebrahimzadeh, (2008). “An Algorithm For Mining Fuzzy Association Rules”. Proceedings of the International Multi Conference of Engineers and Computer Scientists, Vol. 1, pp. 486-490.
[17]. Ashish Mangalampalli, and Vikram Pudi, (2010). “FPrep: Fuzzy Clustering driven Efficient Automated Preprocessing for Fuzzy Association Rule Mining”. IEEE Intl Conference on Fuzzy Systems (FUZZ-IEEE).
[18]. K. Suriya Prabha, and R. Lawrance, (2012). “Mining Fuzzy Frequent itemset using Compact Frequent Pattern (CFP) tree Algorithm”. International Conference on Computing and Control Engineering (ICCCE 2012).
[19]. Sunita Soni, and O.P. Vyas, (2012). “Fuzzy Weighted Associative Classifier: A Predictive Technique For Health Care Data Mining”. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), Vol. 2, No. 1.
[20]. J. Preethi, (2013). “Temporal outlier detection using fuzzy logic and evolutionary computation”. International Conference on Optical Imaging Sensor and Security (ICOSS), pp. 2-3.
[21]. Anubha Sharma, and Nirupama Tiwari, (2014). “A Survey of Fuzzy Based Association Rule Mining to Find Cooccurrence Relationships”. IOSR Journal of Computer Engineering (IOSR-JCE), Vol. 16, No. 1, pp. 83-87.
[22]. Urvi A. Chaudhary, and Mahesh Panchal, (2014). “Mining Multilevel Fuzzy Association Rule from Transaction Data”. International Journal of Computer Science and Mobile Computing, Vol. 3, No. 2, pp. 773-778.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.