Author : Nikhil Gumaste 1
Date of Publication :29th March 2018
Abstract: Discovering information from expansive informational collections to use in intelligent systems turns out to be increasingly essential in the Internet period. Pattern mining, classification, text mining, and opinion mining are the topical issues. Among them, pattern mining is a important issue. The issue of mining erasable patterns (EPs) has been proposed as a variation of frequent pattern mining for optimizing the generation plan of production factories. A few algorithms have been proposed for effectively mining EPs. Be that as it may, for extensive limit esteems, many EPs are acquired, prompting substantial memory use. In this manner, it is important to mine a consolidated portrayal of EPs. This paper first defines erasable closed patterns (ECPs), which can represent to the set of EPs without data loss. At that point, a theorem for quick deciding ECPs in view of dPidset structure is proposed and demonstrated. Next, two efficient algorithms [erasable closed patterns mining (ECPat) and dNC_Set based algorithm for erasable closed patterns mining (dNC-ECPM)] for mining ECPs in view of this theorem are proposed
Reference :
-
- R. Agrawal and R. Srikant, ``Fast algorithms for mining association rules,'' in Proc. VLDB, vol. 94, pp. 487_499, Sep. 1994.
- R. Agrawal, T. Imielinski, and A. Swami, ``Mining association rules between set of items in large databases,'' in Proc. SIGMOD, vol. 93, no. 2, pp. 207_216, 1993.
- Z.-H. Deng and X.-R. Xu, ``Fast mining erasable itemsets using NC_sets,'' Expert Syst. Appl., vol. 39, no. 4, pp. 4453_4463, 2012.
- T.-L. Dam, K. Li, P. Fournier-Viger, and Q.-H. Duong, “An efficient algorithm for mining top-rank-k frequent patterns,” Appl. Intelligent, vol. 45, no. 1, pp. 96_111, 2016.
- J. Han, J. Pei, and Y. Yin,“ Mining frequent patterns without candidate generation,” in Proc. SIGMOD, pp. 1_12, 2000.
- C.-W. Lin and T.-P. Hong,“ Maintenance of pre-large trees for data mining with modified records,'' Inf. Sci., vol. 278, pp. 88_103, Sep. 2014.
- G. Pyun and U. Yun, ``Mining top-k frequent patterns with combination reducing techniques,'' Appl. Intell., vol. 41, no. 1, pp. 76_98, 2014.
- U. Yun and G. Lee, ``Sliding window based weighted erasable stream pattern mining for stream data applications,'' Future Generate. Computer. Syst., vol. 59, pp. 1_20, Jun. 2016
- N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal ``Discovering frequent closed itemsets for association rules,'' in Proc. ICDT, vol. 99. 1999, pp. 398_416.
- V. S. Tseng, B. E. Shie, C. W. Wu, and P. S. Yu, “Efficient algorithms for mining high utility itemsets from transactional databases,” IEEE Trans. Knowledge. Data Eng., vol. 25, no. 8, pp. 1772_1786, Aug. 2013..