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SPPC: a new tree structure for mining erasable patterns in data streams

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Abstract

Discovering Erasable Patterns (EPs) consists of identifying product parts that will produce a small profit loss if their production is stopped. It is a data mining problem that has attracted the attention of numerous researchers in recent years due to the possibility of using EPs to reduce profit loss of manufacturers. Though, many algorithms have been designed to mine EPs, an important limitation of state-of-the-art EP mining algorithms is that they are batch algorithms, that is, they are designed to be applied on static databases. But in real-life applications, databases are dynamic, as they are constantly updated by adding or removing products and parts. To be informed about EPs in real-time, traditional EP mining algorithms must be applied over and over again on a database. This is inefficient as those algorithms are always applied from scratch without taking advantage of results generated by previous executions. Considering this important drawback of previous work for handling real-life dynamic data, this paper proposes an efficient algorithm named MSPPC for mining EPs in data streams. It relies on a novel tree structure named SPPC (Streaming Pre-Post Code) tree, which extends the WPPC tree structure for maintaining a compact tree representation of EPs in a data stream. Experimental results show that the designed MSPPC algorithm outperforms the state-of-the-art batch MERIT and dMERIT algorithms when they are run in batch mode using a sliding-window. Besides, the proposed algorithm is also faster than the state-of-the-art algorithms for mining EPs, namely MERIT, dMERIT + , MEI and EIFDD.

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References

  1. Agarwal V, Bharadwaj KK (2015) Predicting the dynamics of social circles in ego networks using pattern analysis and GA K-means clustering. WIREs: Data Min Knowl Discov 5(3):113–141

    Google Scholar 

  2. Agrawal R, Imielinski T, Swami AN (1993) Mining association rules between sets of items in large databases. In: ACM SIGMOD’93, pp 207–216

  3. Alpar P, Winkelsträter S (2014) Assessment of data quality in accounting data with association rules. Exp Syst Appl 41(5):2259–2268

    Article  Google Scholar 

  4. Chang J, Lee W (2006) Finding recently frequent itemsets adaptively over online transactional data streams. Inf Syst 31(8):849–869

    Article  Google Scholar 

  5. Chang J, Lee W (2009) estMax: tracing maximal frequent itemsets instantly over online transactional data streams. IEEE Trans Knowl Data Eng 21(10):1418–1431

    Article  Google Scholar 

  6. Chen H (2014) Mining top-k frequent patterns over data streams sliding window. J Intell Inf Syst 42(1):111–131

    Article  Google Scholar 

  7. Chen H, Shu L, Xia J, Deng Q (2012) Mining frequent patterns in a varying-size sliding-window of online transactional data streams. Inf Sci 215:15–36

    Article  MathSciNet  Google Scholar 

  8. Chiu S-C, Li H-F, Huang J-L, You H-H (2011) Incremental mining of closed inter-transaction itemsets over data stream sliding windows. J Inf Sci 37(2):208–220

    Article  Google Scholar 

  9. Dakhel AM, Malazi HT, Mahdavi M (2018) A social recommender system using item asymmetric correlation. Appl Intell 48(3):527–540

    Article  Google Scholar 

  10. Deng ZH (2013) Mining top-rank-k erasable itemsets by PID_lists. Int J Intell Syst 28(4):366–379

    Article  Google Scholar 

  11. Deng ZH (2016) DiffNodesets: an efficient structure for fast mining frequent itemsets. Appl Soft Comput 41:214–223

    Article  Google Scholar 

  12. Deng ZH, Xu XR (2012) Fast mining erasable itemsets using NC_sets. Exp Syst Appl 39(4):4453–4463

    Article  Google Scholar 

  13. Deng ZH, Fang G, Wang Z, Xu X (2009) Mining erasable itemsets. In: ICMLC’09, pp 67–73

  14. Deypir M, Sadreddini MH (2011) EclatDS: an efficient sliding-window based frequent pattern mining method for data streams. Intell Data Anal 15(4):571–587

    Article  Google Scholar 

  15. Deypir M, Sadreddini MH, Tarahomi M (2013) An efficient sliding-window based algorithm for adaptive frequent itemset mining over data streams. J Inf Sci Eng 29(5):1001–1020

    MathSciNet  Google Scholar 

  16. Fournier-Viger P, Lin JCW, Vo B, Chi TT, Zhang J, Le HB (2017) A survey of itemset mining. WIREs Data Min Knowl Discov 7(4):e1207

  17. Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: ACM SIGMOD’00, pp 1–12

  18. Khader N, Lashier A, Yoon SW (2016) Pharmacy robotic dispensing and planogram analysis using association rule mining with prescription data. Exp Syst Appl 57:296–310

    Article  Google Scholar 

  19. Le T, Vo B (2014) MEI: an efficient algorithm for mining erasable itemsets. Eng Appl Artif Intell 27:155–166

    Article  Google Scholar 

  20. Le T, Vo B, Coenen F (2013) An efficient algorithm for mining erasable itemsets using the difference of NC-Sets. In: SMC’13, pp 2270–2274

  21. Le T, Vo B, Nguyen G (2014) A survey of erasable itemset mining algorithms. WIREs: Data Min Knowl Discov 4(5):356– 379

    Google Scholar 

  22. Le T, Lee MY, Park JR, Baik SW (2018) Oversampling techniques for bankruptcy prediction: novel features from a transaction dataset. Symmetry 10(4):79

    Article  Google Scholar 

  23. Le HS, Chiclana F, Kumar R, Mittal M, Khari M, Chatterjee JM, Baik SW (2018) ARM-AMO: an efficient association rule mining algorithm based on animal migration optimization. Knowl-Based Syst 154:68–80

    Article  Google Scholar 

  24. Le T, Vo B, Baik SW (2018) Efficient algorithms for mining top-rank-k erasable patterns using pruning strategies and the subsume concept. Eng Appl Artif Intell 68:1–9

    Article  Google Scholar 

  25. Le T, Nguyen A, Huynh B, Vo B, Pedrycz W (2018) Mining constrained inter-sequence patterns: a novel approach to cope with item constraints. Appl Intell 48(5):1327–1343

    Google Scholar 

  26. Lee G, Yun U, Ryu K (2014) Sliding-window based weighted maximal frequent pattern mining over data streams. Exp Syst Appl 41(2):694–708

    Article  Google Scholar 

  27. Lee G, Yun U, Ryang H (2015) Mining weighted erasable patterns by using underestimated constraint-based pruning technique. J Intell Fuzzy Syst 28(3):1145–1157

    Google Scholar 

  28. Lee G, Yun U, Ryang H, Kim D (2016) Erasable itemset mining over incremental databases with weight conditions. Eng Appl Artif Intell 52:213–234

    Article  Google Scholar 

  29. Lin CW, Gan W, Fournier-Viger P, Hong TP, Tseng VS (2016) Weighted frequent itemset mining over uncertain databases. Appl Intell 44(1):232–250

    Article  Google Scholar 

  30. Manku GS, Motwani R (2002) Approximate frequency counts over data streams. In: VLDB’02, pp 346–357

  31. Nanda SJ, Panda G (2015) Design of computationally efficient density-based clustering algorithms. Data Knowl Eng 95:23–38

    Article  Google Scholar 

  32. Nguyen G, Le T, Vo B, Le B (2014) A new approach for mining top-rank-k erasable itemsets. In: ACIIDS’14, pp 73–82

  33. Nguyen G, Le T, Vo B, Le B (2015) Discovering erasable closed patterns. In: ACIIDS’15, pp 368–376

  34. Nguyen G, Le T, Vo B, Le B (2015) EIFDD: an efficient approach for erasable itemset mining of very dense datasets. Appl Intell 43(1):85–94

    Article  Google Scholar 

  35. Nori F, Deypir M, Sadreddini MH (2013) A sliding-window based algorithm for frequent closed itemset mining over data streams. J Syst Softw 86(3):615–623

    Article  Google Scholar 

  36. Ryang H, Yun U (2016) High utility pattern mining over data streams with sliding-window technique. Expert Syst Appl 57:214–231

    Article  Google Scholar 

  37. Sahoo J, Das AK, Goswami A (2015) An efficient approach for mining association rules from high utility itemsets. Exp Syst Appl 42(13):5754–5778

    Article  Google Scholar 

  38. Tsai PSM (2010) Mining top-k frequent closed itemsets over data streams using the sliding-window model. Exp Syst Appl 37(10):6968–6973

    Article  Google Scholar 

  39. Vo B, Le T, Coenen F, Hong TP (2016) Mining frequent itemsets using the N-list and subsume concepts. Int J Mach Learn Cybern 7(2):253–265

    Article  Google Scholar 

  40. Vo B, Le T, Nguyen G, Hong TP (2017) Efficient algorithms for mining erasable closed patterns from product datasets. IEEE Access 5:3111–3120

    Article  Google Scholar 

  41. Wang J, Li H, Huang J, Su C (2016) Association rules mining based analysis of consequential alarm sequences in chemical processes. J Loss Prev Process Ind 41:178–185

    Article  Google Scholar 

  42. Yu JX, Chong Z, Lu H, Zhang Z, Zhou A (2006) A false negative approach to mining frequent itemsets from high speed transactional data streams. Inf Sci 176(14):1986–2015

    Article  Google Scholar 

  43. Yun U, Lee G (2016) Sliding-window based weighted erasable stream pattern mining for stream data applications. Futur Gener Comput Syst 59:1–20

    Article  Google Scholar 

  44. Yun U, Kim D, Ryang H, Lee G, Lee KM (2016) Mining recent high average utility patterns based on sliding-window from stream data. J Intell Fuzzy Syst 30(6):3605–3617

    Article  Google Scholar 

  45. Yun U, Ryang H, Lee G, Fujita H (2017) An efficient algorithm for mining high utility patterns from incremental databases with one database scan. Knowl-Based Syst 124:188–206

    Article  Google Scholar 

  46. 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

    Article  Google Scholar 

  47. Zaki MJ, Hsiao CJ (2005) Efficient algorithms for mining closed itemsets and their lattice structure. IEEE Trans Knowl Data Eng 17(4):462–478

    Article  Google Scholar 

  48. Zhi-Jun X, Hong C, Li C (2006) An efficient algorithm for frequent itemset mining on data streams. In: ICDM’06, pp 474–491

Download references

Acknowledgements

This research was supported by the Korean MSIT (Ministry of Science and ICT), under the National Program for Excellence in SW (2015-0-00938), supervised by the IITP (Institute for Information & communications Technology Promotion).

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Authors and Affiliations

  1. Digital Contents Research Institute, Sejong University, Seoul, Republic of Korea

    Tuong Le, Mi Young Lee & Sung Wook Baik

  2. Division of Data Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Bay Vo

  3. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Bay Vo

  4. School of Natural Sciences and Humanities, Harbin Institute of Technology (Shenzhen), Shenzhen, GD, China

    Philippe Fournier-Viger

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  1. Tuong Le
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  2. Bay Vo
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  3. Philippe Fournier-Viger
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  4. Mi Young Lee
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  5. Sung Wook Baik
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Correspondence to Sung Wook Baik.

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Le, T., Vo, B., Fournier-Viger, P. et al. SPPC: a new tree structure for mining erasable patterns in data streams. Appl Intell 49, 478–495 (2019). https://doi.org/10.1007/s10489-018-1280-5

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  • DOI: https://doi.org/10.1007/s10489-018-1280-5

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