Association-rule mining is commonly used mining technique for finding frequent patterns. In real world applications traditional association-rule mining is not appropriate since the purchased item have different factors, for example, amount and benefit. High utility pattern mining was used for unravelling the limitations of the association-rule mining as far as amount and benefit. There are algorithms for finding high utility patterns from static databases. Some researches worked for handling dynamic dataset but huge computational time and multiple database scan was required. In this paper, a system is proposed for finding high utility patterns which uses list based data structure for dynamic dataset. To improve computational time and memory, constraints like item, date or length are used. A few experiments are led to demonstrate the execution of the proposed system with and without using constraints regarding time and memory.

Association rules (ARs); Association rule mining; frequent patterns; high utility patterns; pattern mining

[1] M. Liu , J.-F. Qu , “Mining high utility itemsets without candidate generation”, in: International Conference on Information and Knowledge Management (CIKM 2012), 2012, pp. 55–64.
[2] J.C.-W. Lin , T. Li , P. Fournier-Viger , T.-P. Hong , J. Zhan , M. Voznak , “An efficient algorithm to mine high average-utility itemsets”, Adv. Eng. Inform. 30 (2) (2016) 233–243.
[3] J. Liu , K. Wang , B.C.M. Fung , “Mining high utility patterns in one phase without generating candidates”, IEEE Trans. Knowl. Data Eng. 28 (5) (2016) 1245–1257 .
[4] J. Liu , K. Wang , B.C.M. Fung , “Direct discovery of high utility itemsets without candidate generation”, in: Proceedings of the 2012 IEEE International Conference on Data Mining (ICDM 2012), 2012, pp. 984–989.
[5] L. Troiano , G. Scibelli , “Mining frequent itemsets in data streams within a time horizon”, Data Knowl. Eng. 89 (2014) 21–37.
[6] C.F. Ahmed , S.K. Tanbeer , B.-S. Jeong , H.-J. Choi , “Interactive mining of high utility patterns over data streams”, Expert Syst. Appl. 39 (15) (2012) 11979–11991.
[7] V.S. Tseng , C.-W. Wu , P. Fournier-Viger , P.S. Yu , “Efficient algorithms for mining the concise and lossless representation of high utility itemsets”, IEEE Trans. Knowl. Data Eng. 27 (3) (2015) 726–739.
[8] J. Sahoo , A.K. Das , A. Goswami , “An efficient approach for mining association rules from high utility itemsets”, Expert Syst. Appl. 42 (13) (2015) 5754–5778.
[9] U. Yun , D. Kim , H. Ryang , G. Lee , K.-M. Lee , “Mining recent high average utility patterns based on sliding window from stream data”, J. Intell. Fuzzy Syst. 30 (6) (2016) 3605–3617.
[10] C.-W. Lin , G.-C. Lan , T.-P. Hong , “Mining high utility itemsets for transaction deletion in a dynamic database”, Intell. Data Anal. 19 (1)(2015) 43–55.
[11] H. Ryang , U. Yun , K. Ryu , “Fast algorithm for high utility pattern mining with the sum of item quantities”, Intell. Data Anal. 20 (2) (2016) 395–415.
[12] U. Yun , D. Kim , H. Ryang , G. Lee , K.-M. Lee , “Mining recent high average utility patterns based on sliding window from stream data”, J. Intell. Fuzzy Syst. 30 (6) (2016) 3605–3617.
[13] R. Agrawal , R. Srikant , “Fast algorithms for mining association rules”, in: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB 1994), 1994, pp. 487–499.