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WIREs Data Mining Knowl Discov

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The lattice‐based approaches for mining association rules: a review

Advanced Review

Tuong Le, Bay Vo

Published Online: May 20 2016

DOI: 10.1002/widm.1181

Full article on Wiley Online Library: HTML PDF

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The traditional methods for mining association rules (ARs) include two phrases: mining frequent itemsets (FIs)/frequent closed itemsets (FCIs)/frequent maximal itemsets (FMIs) and generating ARs from FIs/FCIs/FMIs. Lattice‐based approaches (LBAs) for mining ARs are new approaches including two phrases: frequent itemset lattice (FIL)/frequent closed itemset lattice (FCIL) building and generating ARs from the lattice. Total mining time of LBAs for mining ARs outperforms the traditional methods for mining ARs. Besides, the most important advantage of LBAs for mining ARs is that the algorithms only build the lattice once and mine ARs with many different confidences or many different minimum supports (the thresholds have to be greater than or equal to the threshold used to build lattices) without mining FIs/FCIs again. In this article, we describe a number of existing LBAs for mining ARs on static databases including lattice building and rule generation. In addition, in today's online system, the data often change in several operations such as insert, delete, and update. Hence, a number of LBAs for mining ARs on dynamic databases are mentioned. Finally, complexity analysis of the LBAs for mining ARs is also thoroughly discussed. WIREs Data Mining Knowl Discov 2016, 6:140–151. doi: 10.1002/widm.1181

This article is categorized under:

Algorithmic Development > Association Rules

FIL‐2009 for DB^e with minSup = 50%.

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Minimal non‐redundant association rules generation on node A.

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Association rules with interestingness measures generation on node A.

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Association rules generation on node A.

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FCIL‐2013 for DB^e with minSup = 50%.

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FCIL‐2005 for DB^e with minSup = 50%.

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FIL‐2014 for DB^e with minSup = 50%.

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FIL‐2011 for DB^e with minSup = 50%.

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References

Menardi, G, Torelli, N. Training and assessing classification rules with imbalanced data. Data Min Knowl Discov 2014, 28:92–122.
Nassirtoussi, AK, Aghabozorgi, SR, The Y.W, Ngo, DCL. Text mining for market prediction: a systematic review. Expert Syst Appl 2014, 41:7653–7670.
Niemann, U, Völzke, H, Kühn, JP, Spiliopoulou, M. Learning and inspecting classification rules from longitudinal epidemiological data to identify predictive features on hepatic steatosis. Expert Syst Appl 2014, 41:5405–5415.
Nguyen, TTL, Vo, B, Hong, TP, Hoang, CT. Classification based on association rules: a lattice‐based approach. Expert Syst Appl 2012, 39:11357–11366.
Shindea, S, Kulkarnib, U. Extracting classification rules from modified fuzzy min–max neural network for data with mixed attributes. Appl Soft Comput 2016, 40:364–378.
Wang, X, Liu, X, Pedrycz, W, Zhu, X, Hu, G. Mining axiomatic fuzzy set association rules for classification problems. Eur J Oper Res 2012, 218:202–210.
Le, HS. A novel kernel fuzzy clustering algorithm for Geo‐Demographic Analysis. Inform Sci 2015, 317:202–223.
Mai, TS, He, X, Feng, J, Plant, C, Böhm, C. Anytime density‐based clustering of COMPLEx data. Knowl Inf Syst 2015, 45:319–355.
Indurkhya, N. Emerging directions in predictive text mining. Data Min Knowl Discov 2015, 5:155–164.
Vairavasundaram, S, Varadharajan, V, Vairavasundaram, I, Ravi, L. Data mining‐based tag recommendation system: an overview. Data Min Knowl Discov 2015, 5:87–112.
Fariha, A, Ahmed, CF, Leung, CK, Samiullah, M, Pervin, S, Cao, L. A new framework for mining frequent interaction patterns from meeting databases. Eng Appl Artif Intel 2015, 45:103–118.
Fournier‐Viger, P, Gomariz, A, Gueniche, T, Soltani, A, Wu, CW, Tseng, VS. SPMF: a Java open‐source pattern mining library. J Mach Learn Res 2014, 15:3389–3393.
Hacene, MR, Huchard, M, Napoli, A, Valtchev, P. Relational concept analysis: mining concept lattices from multi‐relational data. Ann Math Artif Intell 2013, 67:81–108.
Lan, GC, Hong, TP, Tseng, VS. An efficient projection‐based indexing approach for mining high utility itemsets. Knowl Inf Syst 2014, 38:85–107.
Lin, CW, Lan, GC, Hong, TP. Mining high utility itemsets for transaction deletion in a dynamic database. Intell Data Anal 2015, 19:43–55.
Lin, CW, Hong, TP, Lan, GC, Wong, JW, Lin, WY. Incrementally mining high utility patterns based on pre‐large concept. Appl Intell 2014, 40:343–357.
Lin, JCW, Gan, W, Hong, TP. A fast updated algorithm to maintain the discovered high‐utility itemsets for transaction modification. Adv Eng Inform 2015, 29:562–574.
Lin, JCW, Gan, W, Hong, TP, Tseng, VS. Efficient algorithms for mining up‐to‐date high‐utility patterns. Adv Eng Inform 2015, 29:648–661.
Song, W, Liu, Y, Li, J. Mining high utility itemsets by dynamically pruning the tree structure. Appl Intell 2014, 40:29–43.
Song, W, Liu, Y, Li, J. BAHUI: fast and memory efficient mining of high utility itemsets based on bitmap. Int J Data Warehouse Min 2014, 10:1–15.
Tseng, VS, Wu, CW, Fournier‐Viger, P, Yu, PS. Efficient algorithms for mining the concise and lossless representation of high utility itemsets. IEEE Trans Knowl Data Eng 2015, 27:726–739.
Yun, U, Ryang, H. Incremental high utility pattern mining with static and dynamic databases. Appl Intell 2015, 42:323–352.
Zhang, X, Deng, ZH. Mining summarization of high utility itemsets. Knowl‐Based Syst 2015, 84:67–77.
Kim, D, Yun, U. Efficient mining of high utility patterns with considering of rarity and length. Appl Intell, in press, doi:10.1007/s10489-015-0750-2.
Ryang, H, Yun, U, Ryu, K. Fast algorithm for high utility pattern mining with the sum of item quantities. Intell Data Anal 2016, 20:395–415.
Ryang, H, Yun, U, Ryu, K. Discovering high utility itemsets with multiple minimum supports. Intell Data Anal 2014, 18:1027–1047.
Yun, U, Ryang, H, Ryu, K. High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates. Expert Syst Appl 2014, 41:3861–3878.
Tseng, VS, Wu, CW, Fournier‐Viger, P, Yu, PS. Efficient algorithms for mining top‐k high utility itemsets. IEEE Trans Knowl Data Eng 2016, 28:54–67.
Lee, G, Yun, U, Ryang, H. An uncertainty‐based approach: frequent itemset mining from uncertain data with different item importance. Knowl‐Based Syst 2015, 90:239–256.
Yun, U, Pyun, G, Yoon, E. Efficient mining of robust closed weighted sequential patterns without information loss. Int J Artif Intell Tools 2015, 24:1–28.
Yun, U, Yoon, E. An efficient approach for mining weighted approximate closed frequent patterns considering noise constraints. Int J Uncertainty Fuzziness Knowl Based Syst 2014, 22:879–912.
Deng, ZH, Xu, X. Fast mining erasable itemsets using NC_sets. Expert Syst Appl 2012, 39:4453–4463.
Le, T, Vo, B, Nguyen, G. A survey of erasable itemset mining algorithms. Data Min Knowl Discov 2014, 4:356–379.
Nguyen, G, Le, T, Vo, B, Le, B. EIFDD: an efficient approach for erasable itemset mining of very dense datasets. Appl Intell 2015, 43:85–94.
Lee, G, Yun, U, Ryang, H. Mining weighted erasable patterns by using underestimated constraint‐based pruning technique. J Intell Fuzzy Syst 2015, 28:1145–1157.
Yun, U, Lee, G. Sliding window based weighted erasable stream pattern mining for stream data applications. Future Gener Comput Syst 2016, 59:1–20.
Le, T, Vo, B. An N‐list‐based algorithm for mining frequent closed patterns. Expert Syst Appl 2015, 42:6648–6657.
Liu, XB, Zhai, K, Pedrycz, W. An improved association rules mining method. Expert Syst Appl 2012, 39:1362–1374.
Pei, J, Han, J, Mao, R. CLOSET: an efficient algorithm for mining frequent closed itemsets. In: 5th ACM‐SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 2000, 21–30.
Tran, A, Truong, CT, Le, B. Simultaneous mining of frequent closed itemsets and their generators: foundation and algorithm. Eng Appl Artif Intel 2014, 36:64–80.
Vo, B, Hong, TP, Le, B. DBV‐Miner: a dynamic bit‐vector approach for fast mining frequent closed itemsets. Expert Syst Appl 2012, 39:7196–7206.
Burdick, D, Calimlim, M, Flannick, J, Gehrke, J, Yiu, T. MAFIA: a maximal frequent itemset algorithm. IEEE Trans Knowl Data Eng 2005, 17:1490–1504.
Gouda, K, Zaki, MJ. GenMax: an efficient algorithm for mining maximal frequent itemsets. Data Min Knowl Discov 2005, 11:223–242.
Yun, U, Lee, G. Incremental mining of weighted maximal frequent itemsets from dynamic databases. Expert Syst Appl 2016, 54:304–327.
Lee, G, Yun, U, Ryu, K. Sliding window based weighted maximal frequent pattern mining over data streams. Expert Syst Appl 2014, 41:694–708.
Yun, U, Lee, G, Ryu, K. Mining maximal frequent patterns by considering weight conditions over data streams. Knowl‐Based Syst 2014, 55:49–65.
Yun, U, Ryu, K. Efficient mining of maximal correlated weight frequent patterns. Intell Data Anal 2013, 17:917–939.
Pyun, G, Yun, U. Mining top‐k frequent patterns with combination reducing techniques. Appl Intell 2014, 41:76–98.
Ryang, H, Yun, U. Top‐k high utility pattern mining with effective threshold raising strategies. Knowl‐Based Syst 2015, 76:109–126.
Deng, ZH. Fast mining top‐rank‐k frequent patterns by using node‐lists. Expert Syst Appl 2014, 41:1763–1768.
Huynh, TLQ, Vo, B, Le, B. An efficient and effective algorithm for mining top‐rank‐k frequent patterns. Expert Syst Appl 2015, 42:156–164.
Duong, VH, Truong, TC, Vo, B. An efficient method for mining frequent itemsets with double constraints. Eng Appl Artif Intel 2014, 27:148–154.
Agrawal, R, Srikant, R. Fast algorithms for mining association rules. In: Proceedings of VLDB Conference, 1994, 487–499
Dong, J, Han, M. BitTableFI: an efficient mining frequent itemsets algorithm. Knowl‐Based Syst 2007, 20:329–335.
Han, J, Pei, J, Yin, Y. Mining frequent patterns without candidate generation. In: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000, 1–12.
Grahne, G, Zhu, J. Fast algorithms for frequent itemset mining using FP‐trees. IEEE Trans Knowl Data Eng 2005, 17:1347–1362.
Zaki, MJ, Parthasarathy, S, Ogihara, M, Li, W. New algorithms for fast discovery of association rules. In: 3rd International Conference on Knowledge Discovery and Data Mining (KDD), 1997, 283–286
Zaki, MJ, Hsiao, CJ. Efficient algorithms for mining closed itemsets and their lattice structure. IEEE Trans Knowl Data Eng 2005, 17:462–478.
Song, W, Yang, B, Xu, Z. Index‐BitTableFI: an improved algorithm for mining frequent itemsets. Knowl‐Based Syst 2008, 21:507–513.
Vo, B, Le, B. Interestingness measures for association rules: combination between lattice and hash tables. Expert Syst Appl 2011, 38:11630–11640.
Deng, Z, Wang, Z, Jiang, JJ. A new algorithm for fast mining frequent itemsets using N‐lists. Sci China Inform Sci 2012, 55:2008–2030.
Deng, ZH, Lv, SL. Fast mining frequent itemsets using nodesets. Expert Syst Appl 2014, 41:4505–4512.
Vo, B, Le, T, Coenen, F, Hong, TP. Mining frequent itemsets using the N‐list and subsume concepts. Int J Mach Learn Cybern, 2016, 7:253–265.
Deng, ZH, Lv, SL. PrePost+: an efficient N‐lists‐based algorithm for mining frequent itemsets via children‐parent equivalence pruning. Expert Syst Appl 2015, 42:5424–5432.
Pasquier, N, Bastide, Y, Taouil, R, Lakhal, L. Efficient mining of association rules using closed itemset lattices. Inf Syst 1999, 24:25–46.
Sahoo, J, Das, AK, Goswami, A. An effective association rule mining scheme using a new generic basis. Knowl Inf Syst 2015, 43:127–156.
Vo, B, Le, B. Mining traditional association rules using frequent itemsets lattice. In: 39th International Conference on Computers %26 Industrial Engineering, 2009, 1401–1406.
Vo, B, Le, B. Mining minimal non‐redundant association rules using frequent itemsets lattice. Int J Intell Syst Technol Appl 2011, 10:92–106.
Vo, B, Le, T, Hong, TP, Le, B. An effective approach for maintenance of pre‐large‐based frequent‐itemset lattice in incremental mining. Appl Intell 2014, 41:759–775.
Vo, B, Hong, TP, Le, B. A lattice‐based approach for mining most generalization association rules. Knowl‐Based Syst 2013, 45:20–30.
Szathmary, L, Valtchev, P, Napoli, A, Godin, R, Boc, A, Makarenkov, V. A fast compound algorithm for mining generators, closed itemsets, and computing links between equivalence classes. Ann Math Artif Intell 2014, 70:81–105.
Hong, TP, Wang, CY, Tao, YH. A new incremental data mining algorithm using pre‐large itemsets. Intell Data Anal 2001, 5:111–129.
La, PT, Le, B, Vo, B. Incrementally building frequent closed itemset lattice. Expert Syst Appl 2014, 41:2703–2712.
Vo, B, Le, T, Hong, TP, Le, B. Fast updated frequent‐itemset lattice for transaction deletion. Data Knowl Eng 2015, 96:78–89.
Gupta, A, Bhatnagar, V, Kumar, N. Mining closed itemsets in data stream using formal concept analysis. In: 12th International Conference on Data Warehousing and Knowledge Discovery, 2010, 285–296.

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