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Articles

Mining Frequent Patterns Partially Devoid of Dissociation with Automated MMS Specification Strategy

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References

  • J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation,” ACM SIGMOD Rec., Vol. 29, no. 2, pp. 1–12, 2000. doi: 10.1145/335191.335372
  • C. H. Chee, J. Jaafar, I. A. Aziz, M. H. Hasan, and W. Yeoh, “Algorithms for frequent itemset mining: a literature review,” Artif. Intell. Rev., Vol. 52, pp. 2603–21, 2019. doi: 10.1007/s10462-018-9629-z
  • J. M. Luna, P. Fournier-Viger, and S. Ventura, “Frequent itemset mining: a 25 years review,” WIREs Data Min. Knowl. Discov., Vol. 9, no. 6, pp. e1329, 2019.
  • J. Zhang, L. Shou, S. Wu, G. Chen, and K. Chen, “A two-phase approach for unexpected pattern mining,” Expert. Syst. Appl., Vol. 141, pp. 112946, 2020. doi: 10.1016/j.eswa.2019.112946
  • R. Agrawal, T. Imieliński, and A. Swami, “Mining association rules between sets of items in large databases,” ACM SIGMOD Rec., Vol. 22, pp. 207–16, 1993. doi: 10.1145/170036.170072
  • S. M. Ghafari, and C. Tjortis, “A survey on association rules mining using heuristics,” WIREs Data Min. Knowl. Discov., Vol. 9, no. 4, pp. e1307, 2019.
  • B. Nath, D. K. Bhattacharyya, and A. Ghosh, “Incremental association rule mining: A survey,” WIREs Data Min. Knowl. Discov., Vol. 3, no. 3, pp. 157–69, 2013. doi: 10.1002/widm.1086
  • A. Borah, and B. Nath, “Rare pattern mining: Challenges and future perspectives,” Complex Intell. Syst., Vol. 5, pp. 1–23, 2019. doi: 10.1007/s40747-018-0085-9
  • A. Borah, and B. Nath, “Rare association rule mining from incremental databases,” Pattern. Anal. Appl., Vol. 23, pp. 113–34, 2020. doi: 10.1007/s10044-018-0759-3
  • Y. S. Koh, and S. D. Ravana, “Unsupervised rare pattern mining: A survey,” ACM TKDD, Vol. 10, no. 4, pp. 1–29, 2016. doi: 10.1145/2898359
  • B. Liu, W. Hsu, and Y. Ma, “Mining association rules with multiple minimum supports,” in SIGKDD Explorations, 1999.
  • Y. C. Lee, T. P. Hong, and W. Y. Lin, “Mining association rules with multiple minimum supports using maximum constraints,” Int. J. Approx. Reason., Vol. 40, no. 1, pp. 44–54, 2005. doi: 10.1016/j.ijar.2004.11.006
  • S. Pal, and A. Bagchi, “Association against dissociation: Some pragmatic consideration for frequent itemset generation under fixed and variable thresholds,” ACM SIGKDD Explor. Newsl., Vol. 7, no. 2, pp. 151–9, 2005. doi: 10.1145/1117454.1117479
  • S. Datta, K. Mali, S. Ghosh, R. Singh, and S. Das, “Interesting pattern mining using item influence,” in Proc. of ICETE’19, Hyderabad, India, 2019, pp. 426–434.
  • S. Datta, and S. Bose, “Discovering association rules partially devoid of dissociation by weighted confidence,” in Proc. IEEE ReTIS, Kolkata, India, 2015, pp. 138–143.
  • R. U. Kiran, and P. K. Reddy, “An improved multiple minimum support based approach to mine rare association rules,” in Proc. IEEE CIDM’09, Nashville, TN, USA, 2009, pp. 340–347.
  • H. Yun, D. Ha, B. Hwang, and K. H. Ryu, “Mining association rules on significant rare data using relative support,” J. Syst. Softw., Vol. 67, no. 3, pp. 181–91, 2003. doi: 10.1016/S0164-1212(02)00128-0
  • S. Darrab, and B. Ergenc, “Frequent pattern mining under multiple support thresholds,” WSEAS Trans. Comput. Res., Vol. 4, pp. 1–10, 2016.
  • Y. H. Hu, and Y. L. Chen, “Mining association rules with multiple minimum supports: A new mining algorithm and a support tuning mechanism,” Decis. Support. Syst., Vol. 42, no. 1, pp. 1–24, 2006. doi: 10.1016/j.dss.2004.09.007
  • M. C. Tseng, and W. Y. Lin, “Efficient mining of generalized association rules with non-uniform minimum support,” Data Knowl. Eng., Vol. 62, no. 1, pp. 41–64, 2007. doi: 10.1016/j.datak.2006.07.002
  • Y. C. Liu, C. P. Cheng, and V. S. Tseng, “Discovering relational-based association rules with multiple minimum supports on microarray datasets,” Bioinformatics., Vol. 27, no. 22, pp. 3142–8, 2011. doi: 10.1093/bioinformatics/btr526
  • R. U. Kiran, and P. K. Reddy, “Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms,” in Proc. of ACM EDBT’11, Uppsala, Sweden, 2011, pp. 11–20.
  • U. K. Rage, and M. Kitsuregawa, “Efficient discovery of correlated patterns using multiple minimum all-confidence thresholds,” J. Intell. Inf. Syst., Vol. 45, no. 3, pp. 357–77, 2015. doi: 10.1007/s10844-014-0314-7
  • W. Gan, J. C. W. Lin, P. Fournier-Viger, H. C. Chao, and J. Zhan, “Mining of frequent patterns with multiple minimum supports,” Eng. Appl. Artif. Intell., Vol. 60, no. C, pp. 83–96, 2017. doi: 10.1016/j.engappai.2017.01.009
  • B. Huynh, C. Trinh, V. Dang, and B. Vo, “A parallel method for mining frequent patterns with multiple minimum support thresholds,” Int. J. Innov. Comput. Inf. Control, Vol. 15, no. 2, pp. 479–88, 2019.
  • Y. C. Lee, T. P. Hong, and T. C. Wang, “Multi-level fuzzy mining with multiple minimum supports,” Expert. Syst. Appl., Vol. 34, no. 1, pp. 459–68, 2008. doi: 10.1016/j.eswa.2006.09.011
  • T. C. K. Huang, “Discovery of fuzzy quantitative sequential patterns with multiple minimum supports and adjustable membership functions,” Inf. Sci. (Ny), Vol. 222, pp. 126–46, 2013. doi: 10.1016/j.ins.2012.07.047
  • S. Darrab, and B. Ergenc, “Vertical pattern mining algorithm for multiple support thresholds,” Procedia. Comput. Sci., Vol. 112, pp. 417–26, 2017. doi: 10.1016/j.procs.2017.08.051
  • H. Zhang, J. Zhang, X. Wei, X. Zhang, T. Zou, and G. Yang, “A new frequent pattern mining algorithm with weighted multiple minimum supports,” Intell. Autom. Soft Comput., Vol. 23, no. 4, pp. 605–12, 2017. doi: 10.1080/10798587.2017.1316082
  • H. Ryang, U. Yun, and K. H. Ryu, “Discovering high utility itemsets with multiple minimum supports,” Intell. Data Anal., Vol. 18, no. 6, pp. 1027–47, 2014. doi: 10.3233/IDA-140683
  • S. Krishnamoorthy, “Efficient mining of high utility itemsets with multiple minimum utility thresholds,” Eng. Appl. Artif. Intell., Vol. 69, pp. 112–26, 2018. doi: 10.1016/j.engappai.2017.12.012
  • E. Yan, and Y. Ding, “Applying centrality measures to impact analysis: A co-authorship network analysis,” J. Am. Soc. Inf. Sci. Technol., Vol. 60, no. 10, pp. 2107–18, 2009. doi: 10.1002/asi.21128
  • S. Datta, and S. Bose, “Mining and ranking association rules in support, confidence, correlation and dissociation framework,” in Proc. of FICTA, Durgapur, India, 2015, pp. 141–151.
  • S. Datta, and K. Mali, “Trust: A new objective measure for symmetric association rule mining in account of dissociation and null transaction,” in Proc. of IEEE ICoAC, Chennai, India, 2017, pp. 151–156.
  • S. X. Dong, L. A. Tang, and Z. Z. Shao, “Research on Evolution Simulation of Technology Innovation Community in high-Tech Zone,” Appl. Mech. Mater., Vol. 411–414, pp. 2472–76, 2013. doi: 10.4028/www.scientific.net/AMM.411-414.2472
  • S. Bagui, and P. C. Dhar, “Positive and negative association rule mining in Hadoop’s MapReduce environment,” J. Big. Data., Vol. 6, 75, 2019. doi: 10.1186/s40537-019-0238-8
  • S. Bag, S. K. Kumar, and M. K. Tiwari, “An efficient recommendation generation using relevant Jaccard similarity,” Inf. Sci., Vol. 483, pp. 53–64, 2019. doi: 10.1016/j.ins.2019.01.023
  • M. Seno, and G. Karypis, “LPMiner: An algorithm for finding frequent itemsets using length-decreasing support constraint,” in Proc. of the IEEE ICDM’01, San Jose, CA, USA, 2001, pp. 505–512.
  • C. S. K. Selvi, and A. Tamilarasi, “Association rule mining with Dynamic-Adaptive support thresholds for associative classification,” in Proc. of ICCIMA’07, Sivakasi, TN, India, 2007, pp. 76–80.
  • S. Datta, K. Mali, and P. Roy, “Ranking of association rules toward smart decision for smart city,” in Proc. of IEEE WiSPNET’17, Chennai, India, 2017, pp. 1398–1403.

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