Abstract
Rare item problem in association rule mining was solved by assigning multiple minimum supports for each item. In the same way rare item problem in correlated pattern mining with all-confidence as interesting measure was solved by assigning multiple minimum all-confidences for each items. In this paper multiple fuzzy correlated pattern tree (MFCP tree) for correlated pattern mining using quantitative transactions is proposed by assigning multiple item all-confidence (MIAC) value for each fuzzy items. As multiple fuzzy regions of a single item are considered, time taken for generating correlated patterns also increases. Difference in Scalar cardinality count for each fuzzy region is considered in calculating MIAC for fuzzy regions. The proposed approach first constructs a multiple frequent correlated pattern tree (MFCP) using MIAC values and generates correlated patterns using MFCP mining algorithm. Each node in MFCP tree serves as a linked list that stores fuzzy items membership value and the super—itemsets membership values of the same path. The outcome of experiments shows that the MFCP mining algorithm efficiently identifies rare patterns that are hidden in multiple fuzzy frequent pattern (MFFP) tree mining technique.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Addison Wesley, Boston (2006)
Agarwal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. The International Conference on Management of Data, pp. 207–216 (1993)
Agarwal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. The International Conference on Very Large Databases, pp. 487–499 (1994)
Kim, E., Kim, W., Lee, Y.: Combination of multiple classifiers for the customer’s purchase behavior prediction. Decis. Support Syst. (2003)
Berkhin, P.: A Survey of clustering data mining techniques. Technical Report, Accrue Software, San Jose, CA (2002)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining Knowl. Discov. (2004)
Zadeh, L.A.: Fuzzy set. Inf. Control 8(3), 338–353 (1965)
de Campos, L.M., Moral, S.: Learning rules for a fuzzy inference model. Fuzzy Sets Syst. 59, 247–257 (1993)
Hong, T.P., Chen, J.B.: Finding relevant attributes and membership functions. Fuzzy Sets Syst. 103, 389–404 (1999)
Hong T.P., Lee, C.Y.: Induction of fuzzy rules and membership functions from training examples. Fuzzy Sets Syst. 33–47 (1996)
Papadimitriou, S., Mavroudi, S.: The fuzzy frequent pattern tree. In: The WSEAS International Conference on Computers (2005)
Hong, T.P., Lin C.W., Lin, T.C.: The MFFP-Tree fuzzy mining algorithm to discover complete linguistic frequent itemsets. Comput. Intell. 30(1) (2014)
Lin, C.W., Hong, T.P., Lu, W.H.: Mining fuzzy association rules based on fuzzy fp-trees. In: The 16th National Conference on Fuzzy Theory and Its Applications, pp. 11–16 (2008)
Lin, C.W., Hong, T.P., Lu, W.H.: An efficient tree-based fuzzy data mining approach. Int. J. Fuzzy Syst. 12(2) (2010)
Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: generalizing association rules to correlations. SIGMOD Rec. 26, 265–276 (1997)
Kim, S., Barsky, M., Han, J.: Efficient mining of top correlated patterns based on null invariant measures. In: ECML PKDD. pp. 172–192 (2011)
Kim, W.Y., Lee, Y.K. Han, J.: Ccmine: efficient mining of confidence-closed correlated patterns. In: PAKDD, pp. 569–579 (2004)
Lee, Y.K., Kim, W.Y., Cao, D., Han, J.: CoMine: efficient mining of correlated patterns. In: ICDM, pp. 581–584 (2003)
Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: KDD, pp. 337–341 (1999)
Rage, U.K., Kitsuregawa, M.: Efficient discovery of correlated patterns using multiple minimum all-confidence thresholds. J. Intell. Inf. Syst. (2014)
Nancy P. Lin., Hao-En Chueh.: Fuzzy correlation rules mining. In: International Conference on Applied Computer Science, Hangzhou, China, 15–17 April (2007)
Han, J., Kamber, W.: Data Mining: Concepts and Techniques. Morgan Kaufman, San Francisco (2001)
Hong, T.P., Kuo, C.S., Wang, S.L.: A fuzzy aprioritid mining algorithm with reduced computational time. Appl. Soft Comput. 5, 1–10 (2004)
Lin, J.C., Hong, T.P., Lin, T.C.: CMFFP-tree algorithm to mine complete multiple fuzzy frequent itemsets. Appl. Soft Comput. (2015)
Frequent Itemset Mining Implementations repository: http://fimi.helsinki.fi
Acknowledgments
The authors like to thank the anonymous referees for their notable comments and Sri Ramakrishna Engineering College for providing resources for our experiments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Anuradha, R., Rajkumar, N., Sowmyaa, V. (2015). Multiple Fuzzy Correlated Pattern Tree Mining with Minimum Item All-Confidence Thresholds. In: Ravi, V., Panigrahi, B., Das, S., Suganthan, P. (eds) Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015). Advances in Intelligent Systems and Computing, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-319-27212-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-27212-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27211-5
Online ISBN: 978-3-319-27212-2
eBook Packages: Computer ScienceComputer Science (R0)