Mining correlated high utility-frequent association rules under various discount notations

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Association analysis is effective to explore relationships or similarities between items that are concealed in massive datasets. The uncovered associations can be characterized as association rules. i.e. discovering new-opportunities for cross-selling the product. Various algorithms elaborate high utility association rules as positive utility values. In real-life appliances, however, a high utility association rules may be associated with items having negative utility values when discounts are considered for certain products. This abundantly hinders their profits for various real-time appliances such as cross-selling or product recommendations so, finding high utility-frequent itemsets under various discount notations is significant for exploring interesting patterns. Also, a well-known constraint of association rules that are determined by using frequent patterns or utility patterns is that, they do not yield a measure of lift to find correlation between items. In this paper, we introduce a novel algorithm called HUFARM-N (High utility-frequent association rule mining with Negative utility values) which incorporates several expansions to mine high utility-frequent association rules that can meet the business profits ensuing to firms. Empirical analysis on real world datasets exhibits that, HUFARMN is highly capable and also enhances both execution time and memory usage.

     

     

     

     

  • Keywords


    Association Rules; Frequent Pattern Mining; Utility Mining.

  • References


      [1] Zaki, Mohammed Javeed. "Scalable algorithms for association mining." IEEE Transactions on Knowledge and Data Engineering 12, no. 3 (2000): 372-390. https://doi.org/10.1109/69.846291.

      [2] Pasquier, Nicolas, Yves Bastide, Rafik Taouil, and Lotfi Lakhal. "Discovering frequent closed itemsets for association rules." In International Conference on Database Theory, pp. 398-416. Springer Berlin Heidelberg, 1999.

      [3] Han, Jiawei, Jian Pei, and Yiwen Yin. "Mining frequent patterns without candidate generation." In ACM Sigmod Record, vol. 29, no. 2, pp. 1-12. ACM, 2000.

      [4] Shankar, S., Nishanth Babu, T. Purusothaman, and S. Jayanthi. "A fast algorithm for mining high utilityitemsets”. In AdvanceComputingConference, 2009. IACC 2009. IEEE International, pp. 1459-1464. IEEE, 2009.

      [5] Zida, Souleymane, Philippe Fournier-Viger, Jerry Chun-Wei Lin, Cheng-Wei Wu, and Vincent S. Tseng. "EFIM: a fast and memory efficient algorithm for high-utility itemset mining." Knowledge and Information Systems (2016): 1-31.

      [6] Fournier-Viger, Philippe, Cheng-Wei Wu, and Vincent S. Tseng. "Mining top-k association rules." In Canadian Conference on Artificial Intelligence, pp. 61-73. Springer Berlin Heidelberg, 2012.

      [7] Chen, Yen-Liang, Jen-Ming Chen, and Ching-Wen Tung. "A data mining approach for retail knowledge discovery with consideration of the effect of shelf-space adjacency on sales." Decision Support Systems 42, no. 3 (2006): 1503-1520. https://doi.org/10.1016/j.dss.2005.12.004.

      [8] Song, Hee Seok, Jae kyeong Kim, and Soung Hie Kim."Mining the change of customer behavior in an internet shopping mall." Expert Systems with Applications 21, no. 3 (2001): 157-168. https://doi.org/10.1016/S0957-4174(01)00037-9.

      [9] Liu, Bing, Wynne Hsu, and Yiming Ma. "Mining association rules with multiple minimum supports.”In Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 337-341. ACM, 1999. https://doi.org/10.1145/312129.312274.

      [10] Lee, Dongwon, Sung-Hyuk Park, and Songchun Moon. "High-Utility Rule Mining for Cross-Selling." In System Sciences (HICSS), 2011 44th Hawaii International Conference on, pp. 1-10. IEEE, 2011.

      [11] Li, Yao, Zhiheng Zhang, Wenbin Chen, and Fan Min. "Mining high utility itemsets with discount strategies." JOURNAL OF INFORMATION &COMPUTATIONAL SCIENCE 11, no. 17 (2014): 6297-6307. https://doi.org/10.12733/jics20104994.

      [12] Fournier-Viger, Philippe. "FHN: efficient mining of high-utility itemsets with negative unit profits." In International Conference on Advanced Data Mining and Applications, pp. 16-29. Springer, Cham, 2014.

      [13] Goyal, Vikram, Ashish Sureka, and Dhaval Patel."Efficient skyline itemsets mining." In Proceedings of the Eighth International C* Conference on Computer Science & Software Engineering, pp. 119-124. ACM, 2015.

      [14] Lin, Jerry Chun-Wei, Lu Yang, Philippe Fournier-Viger, Siddharth Dawar, Vikram Goyal, Ashish Sureka, and Bay Vo. "A More Efficient Algorithm to Mine Skyline Frequent-Utility Patterns." In International Conference on Genetic and Evolutionary Computing, pp. 127-135. Springer International Publishing, 2016.

      [15] Wu, Chieh-Ming, and Yin-Fu Huang. "Generalized association rule mining using an efficient data structure." Expert Systems with Applications 38, no. 6 (2011): 7277-7290. https://doi.org/10.1016/j.eswa.2010.12.023.

      [16] Agrawal, Rakesh, and Ramakrishnan Srikant. "Fast algorithms for mining association rules." In Proc. 20th int. conf. very large databases, VLDB, vol. 1215, pp. 487-499. 1994.

      [17] Yao, Hong, Howard J. Hamilton, and Cory J. Butz. "A foundational approach to mining itemset utilities from databases." In Proceedings of the 2004 SIAM International Conference on Data Mining, pp. 482-486. Society for Industrial and Applied Mathematics, 2004. https://doi.org/10.1137/1.9781611972740.51.

      [18] Liu,Ying,Wei-keng Liao, and Alok Choudhary. "A two-phase algorithm for fast discovery of high utility itemsets." In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 689-695. Springer Berlin Heidelberg, 2005. https://doi.org/10.1007/11430919_79.

      [19] Fournier-Viger, Philippe, Cheng-Wei Wu, Souleymane Zida, and Vincent S. Tseng. "FHM: Faster high-utility itemset mining using estimated utility co-occurrence pruning."In International Symposium on Methodologies for Intelligent Systems, pp. 83-92. Springer International Publishing, 2014.

      [20] Zida, Souleymane, Philippe Fournier-Viger, Jerry Chun-Wei Lin, Cheng-Wei Wu, and Vincent S. Tseng. "EFIM: a fast and memory efficient algorithm for high-utility itemset mining." Knowledge and Information Systems (2016): 1-31.

      [21] Xiong, Hui, Mark Brodie, and Sheng Ma. "Top-cop: Mining top-k strongly correlated pairs in large databases."In Data Mining, 2006. ICDM'06. Sixth International Conference on, pp. 1162-1166. IEEE, 2006.

      [22] Xiong, Hui, P-N. Tan and Vipin Kumar. "Mining strong affinity association patterns in data sets with skewed support distribution." In Data Mining, 2003. ICDM 2003. Third IEEE International Conference on, pp. 387-394. IEEE, 2003.

      [23] Fournier-Viger, Philippe, Cheng-Wei Wu, and Vincent S. Tseng. "Mining top-k association rules." In Canadian Conference on Artificial Intelligence, pp. 61-73. Springer Berlin Heidelberg, 2012.

      [24] Tseng, Vincent S., Cheng-Wei Wu, Philippe Fournier-Viger, and S. Yu Philip. "Efficient algorithms for mining top-k high utility itemsets." IEEE Transactions on Knowledge and Data Engineering 28, no. 1 (2016): 54-67. https://doi.org/10.1109/TKDE.2015.2458860.

      [25] Zida, Souleymane, Philippe Fournier-Viger, Cheng-Wei Wu, Jerry Chun-Wei Lin, and Vincent S. Tseng. "Efficient mining of high-utility sequential rules." In International Workshop on Machine Learning and Data Mining in Pattern Recognition, pp. 157-171. Springer International Publishing, 2015. https://doi.org/10.1007/978-3-319-21024-7_11.

      [26] Yun, Hyunyoon, Danshim Ha, Buhyun Hwang, and Keun Ho Ryu. "Mining association rules on significant rare data using relative support." Journal of Systems and Software 67, no. 3 (2003): 181-191. https://doi.org/10.1016/S0164-1212(02)00128-0.


 

View

Download

Article ID: 11722
 
DOI: 10.14419/ijet.v7i4.11722




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.