Abstract
The problem of mining frequent weighted itemsets (FWIs) is an extension of the mining frequent itemsets (FIs), which considers not only the frequent occurrence of items but also their relative importance in a dataset. However, like mining FIs, mining FWIs usually produces a large result set, which makes it difficult to extract rules and creates redundancy. The problem of mining frequent weighted closed itemsets (FWCIs) has been proposed as a solution to this issue, which produces a smaller result set while preserving sufficient information to extract rules. The weighted node-list (WN-list) structure is currently considered the state-of-the-art structure for mining FWIs. In this study, we first propose the definition of WN-list ancestral operation and a theorem as the theoretical basis for eliminating unsatisfactory candidates, then propose an efficient algorithm, namely NFWCI, for mining FWCIs using the WN-list and an early pruning strategy. The experimental results on many sparse and dense datasets show that the proposed algorithm outperforms the-state-of-the-art algorithm for mining FWCIs.
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References
Bhavithra JSA (2019) Personalized web page recommendation using case-based clustering and weighted association rule mining. Clust Comput 22:6991–7002
Gan W, Lin JCW, Fournier-Viger P, Chao HC, Yu PS (2020) HUOPM: high-utility occupancy pattern mining. IEEE Transactions on Cybernetics 50(3):1195–1208
Kiran RU, Reddy PPC, Zettsu K, Toyoda M, Kitsuregawa M, Reddy PK (2020) Efficient discovery of weighted frequent neighborhood itemsets in very large spatiotemporal databases. IEEE Access 8:27584–27596
Lakshmi KS, Vadivu G (2019) A novel approach for disease comorbidity prediction using weighted association rule mining, J Ambient Intell Humaniz Comput, pp. 1–8
Vanahalli MK, Patil N (2020) Distributed load balancing frequent colossal closed itemset mining algorithm for high dimensional dataset. Journal of Parallel and Distributed Computing 144:136–152
Vo B, Bui H, Vo T, Le T (2020) Mining top-rank-k frequent weighted itemsets using WN-list structures and an early pruning strategy. Knowl-Based Syst 201-202:106064
Deng Z (2016) DiffNodesets: an efficient structure for fast mining frequent itemsets. Appl Soft Comput 41:214–223
Fournier-Viger P, Lin JCW, Vo B, Chi TT, Zhang J, Le HB (2017) A survey of itemset mining, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 7, no. 4
Fourier-Viger P, Li Z, Lin JC-W, Kiran RU, Fujita H (2019) Efficient algorithms to identify periodic patterns in multiple sequences. Inf Sci 489:205–226
Han J, Pei J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, Hsu M-C (2001) Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth, in The 17th International Conference on Data Engineering, Heidelberg, Germany
Jiang C, Coenen F, Zito M (2013) A survey of frequent subgraph mining algorithms. Knowl Eng Rev 28(1):75–105
Duneja E, Sachan AK (2012) A survey on frequent itemset mining with association rules. International Journal of Computer Applications 46(23):18–24
Bui H, Vo B, Nguyen H, Nguyen-Hoang TA, Hong TP (2018) A weighted N-list-based method for mining frequent weighted itemsets. Expert Syst Appl 96:388–405
Gan W, Lin JCW, Fourier-Viger P, Chao HC, Zhan J, Zhang J (2018) Exploiting highly qualified pattern with frequency and weight occupancy. Knowl Inf Syst 56(1):165–196
Gan W, Lin JCW, Fourier-Viger P, Chao HC, Wu JMT, Zhan J (2017) Extracting recent weighted-based patterns from uncertain temporal databases. Eng Appl Artif Intell 61:161–172
Lee G, Yun U, Ryu K (2017) Mining frequent weighted itemsets without storing transaction IDs and generating candidates. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25(1):111–144
Lin JCW, Gan W, Fourier-Viger P, Hong TP, Chao HC (2017) Mining weighted frequent itemsets without candidate generation in uncertain databases. International Journal of Information Technology & Decision Making 16(6):1549–1579
Lin JCW, Gan W, Fournier-Viger P, Hong TP (2015) RWFIM: recent weighted-frequent itemsets mining. Eng Appl Artif Intell 45:18–32
Nguyen H, Vo B, Nguyen M, Pedrycz W (2016) An efficient algorithm for mining frequent weighted itemsets using interval word segments. Appl Intell 45(4):1008–1020
Vo B, Coenen F, Le B (2013) A new method for mining frequent weighted itemsets based on WIT-trees. Expert Syst Appl 40(4):1256–1264
Fournier-Viger P, Zhang Y, Lin JCW, Hamido F, Koh YS (2019) Mining local and peak high utility itemsets. Inf Sci 481:344–367
Gan W, Lin JCW, Fourier-Viger P, Chao HC, Tseng VS, Yu PS (2019) A survey of utility-oriented pattern mining, IEEE Trans Knowl Data Eng, pp. 1–20
Gan W, Lin JC-W, Fournier-Viger P, Chao H-C, Hong T-P, Fujita H (2018) A survey of incremental high-utility itemset mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8(2):e1242
Nguyen LTT, Nguyen P, Nguyen TDD, Vo B, Fournier-Viger P, Tseng VS (2019) Mining high-utility itemsets in dynamic profit databases. Knowl-Based Syst 175:130–144
Yun U, Kim D, Yoon E, Fujita H (2018) Damped window based high average utility pattern mining over data streams. Knowl-Based Syst 144:188–205
Yun U, Nam H, Kim J, Kim H, Baek Y, Lee J, Yoon E, Truong T, Vo B, Pedrycz W (2020) Efficient transaction deleting approach of pre-large based high utility pattern mining in dynamic databases. Futur Gener Comput Syst 103:58–78
Tao F, Murtagh F Farid M (2003) Weighted association rule mining using weighted support and significance framework, in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC
Le T, Vo B (2015) An N-list-based algorithm for mining frequent closed patterns. Expert Syst Appl 42(19):6648–6657
Pasquier N, Bastide Y, Taouil R, Lakhal L (1999) Discovering frequent closed itemsets for association rules, in Database Theory - ICDT'99, Jerusalem, Israel
Pei J, Han J Mao R (2000) CLOSET: an efficient algorithm for mining frequent closet itemsets, in 2000 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Dallas, Texas, USA
Vo B, Hong L, Le B (2012) DBV-miner: a dynamic bit-vector approach for fast mining frequent closed itemsets. Expert Syst Appl 39(8):7196–7206
Vo B (2017) An efficient method for mining frequent weighted closed itemsets from weighted item transaction databases. Journal of Information Science & Engineering 33(1):199–216
Dam TL, Li K, Fournier-Viger P, Duong QH (2019) CLS-miner: efficient and effective closed high-utility itemset mining. Frontiers of Computer Science 13(2):357–381
Fournier-Viger PZS, Lin JCW, Wu CW, Tseng VS (2016) EFIM-closed: fast and memory efficient discovery of closed high-utility itemsets, in International Conference on Machine Learning and Data Mining in Pattern Recognition, New York
Nguyen LTT, Vu VV, Lam MTH, Duong TTM, Manh LT, Nguyen TTT, Vo B, Fujita H (2019) An efficient method for mining high utility closed itemsets. Inf Sci 495:78–99
Wei T, Wang B, Zhang Y, Hu K, Yao Y, Liu H (2020) FCHUIM: efficient frequent and closed high-utility itemsets mining. IEEE Access 8:109928–109939
Ramkumar GD, Ranka S, Tsur S (1998) "Weighted association rules: model and algorithm," in Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98). New York City, New York
Yun U, Leggett JJ (2005) WFIM: weighted frequent itemset mining with a weight range and a minimum weight, in Proceedings of the 2005 SIAM International Conference on Data Mining
Lan GC, Hong TP, Lee HY, Lin CW (2015) Tightening upper bounds for mining weighted frequent itemsets. Intelligent Data Analysis 19(2):413–429
Li Z, Chen F, Wu J, Liu Z, Liu W (2020) Efficient weighted probabilistic frequent itemset mining in uncertain databases, Expert Systems, p. e12551
Zaki M, Hsiao C (2005) Efficient algorithm for mining closed itemsets and their lattice structure. IEEE Transaction on Knowledge and Data Engineering 17(4):462–478
Zaki MJ, Gouda K (2003) "Fast vertical mining using diffsets, in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA
Deng Z, Wang Z, Jiang J (2012) A new algorithm for fast mining frequent itemsets using N-lists. SCIENCE CHINA Inf Sci 55(9):2008–2030
Wang J, Han J, Pei J (2003) CLOSET+ : searching for best strategies for mining frequent closed itemsets," in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA
Grahne G, Zhu J (2005) Fast algorithms for frequent itemset mining using FP-trees. IEEE Trans Knowl Data Eng 17(10):1347–1362
Lucchese C, Orlando S, Perego R (2006) Fast and memory efficient mining of frequent closed itemsets. IEEE Trans Knowl Data Eng 18(1):21–36
Hashem T, Karim MR, Samiullah M, Ahmed CF (2017) An efficient dynamic superset bit-vector approach for mining frequent closed itemsets and their lattice structure. Expert Syst Appl 67:252–271
Vanahalli MK, Patil N (2019) An efficient parallel row enumerated algorithm for mining frequent colossal closed itemsets from high dimensional datasets. Inf Sci 496:343–362
Deng Z, Lv S (2014) Fast mining frequent itemsets using Nodesets. Expert Syst Appl 41(10):4505–4512
Huynh-Thi-Le Q, Le T, Vo B, Le B (2015) An efficient and effective algorithm for mining top-rank-k frequent patterns. Expert Syst Appl 42(1):156–164
Zaki MJ (2004) Mining non-redundant association rules. Data Min Knowl Disc 9(3):223–248
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Bui, H., Vo, B., Nguyen-Hoang, TA. et al. Mining frequent weighted closed itemsets using the WN-list structure and an early pruning strategy. Appl Intell 51, 1439–1459 (2021). https://doi.org/10.1007/s10489-020-01899-7
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DOI: https://doi.org/10.1007/s10489-020-01899-7