Abstract
Existing methods in association rule mining based on traditional support-confidence framework generates huge number of frequent patterns and association rules often ignoring the dissociation among items. Moreover these procedures are unable to order the rules by comparing them to find which one is better than whom. We have introduced a new algorithm for mining frequent patterns based on support and dissociation and thereafter generating rules based on confidence and correlation. The association rules have been ranked based on a composite index computed from the four measures. The experimental results obtained after implementation of the proposed algorithm justify our approach.
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Notes
- 1.
Togetherness is the ratio of association to association plus dissociation. Togetherness is same as Jaccard Similarity coefficient.
- 2.
ws is defined as \( \Phi {\text{s}}\,{ + }\,\left( {1 -\Phi } \right){\rm j} \) where \( {\varPhi }={{\text{f}_{00} } \mathord{\left/ {\vphantom {{\text{f}_{00} } {\text{N}}}} \right. } {\text{N}}} \) i.e., percentage of null transactions in the database for the rule.
- 3.
If the two variables are independent then ρ equals 0. ρ = +1 signifies positive correlation and \( \uprho = {-}1 \) signifies negative correlation.
- 4.
These datasets can be downloaded from http://wiki.csc.calpoly.edu/datasets/wiki/.
- 5.
In the figures X-Axis represents max dissociation, Primary Y-Axis represents frequent itemsets & Rules and Secondary Y-Axis represents Max and Min Rank Index.
References
Agarwal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large datasets. In: Proceedings of the ACM SIGMOD’93, pp. 207–216 (1993)
Agarwal, R., Srikant, R.: Fast algorithms for mining association rules. In: 20th VLDB conference, pp. 487–499. Santiago, Chile (1994)
Pei, J., Han, J.: Mining frequent patterns by patterns-growth: methodology and implications. In: ACM SIGKDD, Dec 2000
Han, J., Pei, J., Yin, Y.: Mining Frequent patterns without candidate generation. In: Proceedings of the ACM SIGMOD (2000)
Craus, M., Archip, A.: A generalized parallel algorithm for frequent itemset mining. In: 12th WSEAS International Conference on Computers, Heraklion, Greece, 23–25 July 2008
Antonie M.L., Zaiane, O.R.: Mining positive and negative association rules: an approach for confined rules. In: Proceedings of the International Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 27–38 (2004)
Wu, X., Zhang, C., Zhang, S.: Efficient mining both positive and negative association rules. ACM Trans. Inf. Syst. 22(3), 381–405 (2004)
Sun, K., Bai, F.: Mining weighted association rules without pre-assigned weight. IEEE Trans. Knowl. Data Eng. 20(4), 489–495 (2008)
Ramaraj, E., Rameshkumar, K.: Ranking mined association rule: a new measure. JTES 1(1), 57–61 (2009)
Lavrac, N., Flach, P., Zupan, B.: Rule Evaluation Measure: A Unifying View. ILP-99, LNAI vpl, vol. 1634, pp. 174–185, Springer, Heidelberg (1999)
Zheng, Z., Kohavi, R., Mason, L.: Real world performance of association rule algorithms. In: Proceedings of the Seventh ACM-SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY (2001)
Pal, S., Bagchi, A.: Association against dissociation: some pragmatic consideration for frequent itemset generation under fixed and variable thresholds. ACM SIGKDD Explor. 7(2), 151–159 (2005)
Bose, S., Datta, S.: Frequent pattern generation in association rule mining using weighted support. In: IEEE C3IT, pp. 1–5 (2015)
Jaccard Index from Wikipedia http://en.wikipedia.org/wiki/Jaccard_index
Webb, G.I., Zhang, S.: K-optimal rule discovery. Data Min. Knowl. Discov. 10(1), 39–79 (2005)
Vo, B., Le, B.: Fast algorithm for mining generalized association rules. Int. J. Database Theory Appl. 2(3), 1–10 (2009)
Das, A., Ng, W.K., Woon, Y.K.: Rapid association rule mining. In: ACM CIKM, Atlanta, GA, USA, 5–10 Nov 2001
Tzanis, G., Berberidis, C., Vlahavas, I.: On the discovery of mutually exclusive items in a market basket database. In: Proceedings of the 2nd ADBIS Workshop on Data Mining and Knowledge Discovery (ADMKD 2006), Thessaloniki, Greece, 06 Sept 2006
Li, W., Han, J., Pei, J.: CMAR: accurate and efficient classification based on multiple class-association rules. In: Proceedings of the 2001 IEEE International Conference on Data Mining (Proceeding ICDM’01), pp. 369–376 (2001)
Tan, P.N., Kumar, V., Srivastava, J.: Selecting the right interestingness measure for association patterns. In: ACM SIGKDD’02, Edmonton, Alberta, Canada (2002)
Fournier-Viger, P., Wu, C.W., Tseng, V.S.: Mining Top-K association rules. In: Proceedings of the 25th Canadian Conference on Advances in Artificial Intelligence (Proceeding Canadian AI’12), pp. 61–73. Springer, Berlin, Heidelberg, ©2012
Fournier-Viger, P., Tseng, V.S.: Mining Top-K non-redundant association rules. In: ISMIS 2012, LNCS, vol. 7661, pp. 31–40. Springer, Heidelberg (2012)
Mallik, S., Mukhopadhyay, A., Maulik, U.: RANWAR: rank-based weighted association rule mining from gene expression and methylation data. IEEE Trans. Nanobiosci. 14(1), 59–66 (2014)
Wang, J., Karypis, G.: HARMONY: efficiently mining the best rules for classification. In: Proceedings of the 2005 SIAM Conference on Data Mining, pp. 205–216 (2005)
Morzy, M.: Efficient mining of dissociation rules. In: DaWaK 2006, LNCS, vol. 4081, pp. 228–237. Springer, Heidelberg (2006)
Datta, S., Bose, S.: Discovering association rules partially devoid of dissociation by weighted confidence. In: IEEE 2nd International Conference on Recent Trends in Information Systems, Jadavpur University, India, 9–11 July, 2015 (in press)
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Datta, S., Bose, S. (2016). Mining and Ranking Association Rules in Support, Confidence, Correlation, and Dissociation Framework. In: Das, S., Pal, T., Kar, S., Satapathy, S., Mandal, J. (eds) Proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA) 2015. Advances in Intelligent Systems and Computing, vol 404. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2695-6_13
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