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An innovative model to mine asynchronous periodic pattern of moving objects

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Abstract

Periodic detection in spatiotemporal data is one of the research focuses in data mining. Most previous works only focused on mining periodic patterns and hardly recognized misaligned presence of a pattern due to the intervention of repetitive data. A more flexible asynchronous periodic patterns mining model (AP2M2) based on clustering algorithm and SMCA algorithm is proposed which mainly has three steps, discovering the invisible repetitive data and clustering them into a single record to generate standard and usable dataset, finding the set of stopovers and mining the periodic patterns of moving objects at each stopover. In the experiment, the Chinese bird-watching data is used to check the effectiveness of AP2M2 model and the results show that the AP2M2 model can precisely mine the asynchronous periodic patterns with low time complexity.

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References

  1. Alvares LO, Oliveira G, Heuser CA, Bogorny V (2009) A Framework for Trajectory Data Preprocessing for Data Mining. International Conference on Software Engineering & Knowledge Engineering, DBLP, pp 698–702

  2. Aref WG, Elfeky MG, Elmagarmid AK (2004) Incremental, online, and merge mining of partial periodic patterns in time-series databases. Knowledge & Data Engineering IEEE Transactions 16(3):332–342

    Article  Google Scholar 

  3. Aydin B, Angryk R (2016) Spatiotemporal Frequent Pattern Mining on Solar Data: Current Algorithms and Future Directions. IEEE International Conference on Data Mining Workshop, IEEE, pp. 575–581

  4. Bodon F (2003) A fast apriori implementation. Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations

  5. Borgelt C (2005) An implementation of the FP-growth algorithm, pp. 1–5

  6. Celik M, Shekhar S, Rogers JP, Shine JA, Kang JM (2007) Mining at most top-k% mixed-drove spatio-temporal co-occurrence patterns 565–574

  7. Celik M, Shine JA, Shine JA, Shine JA (2008) Mixed-drove spatiotemporal co-occurrence pattern mining. IEEE Trans Knowl Data Eng 20(10):1322–1335

    Article  Google Scholar 

  8. Chok H, Le G (2009) An online spatio-temporal association rule mining framework for analyzing and estimating sensor data. International Database Engineering and Applications Symposium, DBLP, pp. 217–226

  9. Cushman SA (2010) Animal Movement Data: GPS Telemetry, Autocorrelation and the Need for Path-Level Analysis. Spatial Complexity, Informatics, and Wildlife Conservation, Springer Japan 2010:131–149

    Article  Google Scholar 

  10. Ester M, Kriegel HP, Xu X (1996) A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. International Conference on Knowledge Discovery and Data Mining, AAAI Press, pp 226–231

  11. Guan Y, Xia S, Lei Z, Mu Z, Cheng J (2012) Asynchronous periodic patterns discovery for moving objects. Journal of Convergence Information Technology 7(9):286–294

    Article  Google Scholar 

  12. Han J, Cheng H, Xin D, Yan X (2007) Frequent pattern mining: current status and future directions. Data Min Knowl Disc 15(1):55–86

    Article  MathSciNet  Google Scholar 

  13. Han J, Dong G, Yin Y (1999) Efficient Mining of Partial Periodic Patterns in Time Series Database. International Conference on Data Engineering, IEEE Xplore, pp. 106–115

  14. Han J, Li Z, Tang LA (2010) Mining moving object, trajectory and traffic data. International Conference on Database Systems for Advanced Applications Vol. 5982, Springer-Verlag, pp 485–486

  15. Hartigan JA, Wong MA (1979) Algorithm as 136: a k-means clustering algorithm. J R Stat Soc 28(1):100–108

    MATH  Google Scholar 

  16. He Q, Wang Q, Zhuang F, Tan Q, Shi Z (2011) Parallel clarans clustering based on mapreduce. Energy Procedia 13:3269–3279

    Article  Google Scholar 

  17. Huang KY, Chang CH (2005) Smca: a general model for mining asynchronous periodic patterns in temporal databases. IEEE Transactions on Knowledge & Data Engineering 17(6):774–785

    Article  Google Scholar 

  18. Huang H, Liu F, Zha X, Xiong X, Ouyang T, Liu W et al (2018) Robust bad data detection method for microgrid using improved elm and dbscan algorithm. Journal of Energy Engineering 144(3)

    Article  Google Scholar 

  19. Jeung H, Liu Q, Shen HT, Zhou, X (2008) A Hybrid Prediction Model for Moving Objects. IEEE International Conference on Data Engineering, pp 70–79

  20. Kellaris G, Pelekis N, Theodoridis Y (2013) Map-matched trajectory compression. J Syst Softw 86(6):1566–1579

    Article  Google Scholar 

  21. Lee AJT, Chen YA (2009) Mining frequent trajectory patterns in spatial–temporal databases. Inf Sci 179(13):2218–2231

    Article  Google Scholar 

  22. Lee G, Yang W, Lee JM (2006) A parallel algorithm for mining multiple partial periodic patterns. Inf Sci 176(24):3591–3609

    Article  Google Scholar 

  23. Li Z, Wang J, Han J (2015) Mining event periodicity from incomplete observations. IEEE Transactions on Knowledge & Data Engineering 27(5):1219–1232

    Article  Google Scholar 

  24. Maqbool F, Bashir S, Baig AR (2006) E-map: efficiently mining asynchronous periodic patterns. International Journal of Computer Science & Network Security 6(8A):174–179

    Google Scholar 

  25. Min F, Zhang ZH, Zhai WJ, Shen RP (2018) Frequent pattern discovery with tri-partition alphabets. Inf Sci. https://doi.org/10.1016/j.ins.2018.04.013

    Article  Google Scholar 

  26. Monreale A, Pinelli F, Trasarti R, Giannotti F (2009) Where Next: a location predictor on trajectory pattern mining. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp 637–646

  27. Ng Raymond T, Han J (2002) Clarans: a method for clustering objects for spatial data mining. IEEE Transactions on Knowledge & Data Engineering 14(5):1003–1016

    Article  Google Scholar 

  28. Nishi MA, Ahmed CF, Samiullah M, Jeong BS (2013) Effective periodic pattern mining in time series databases. Expert Syst Appl 40(8):3015–3027

    Article  Google Scholar 

  29. O'Callaghan L, Meyerson A, Motwani R, Mishra N, Guha S (2002) Streaming-Data Algorithms for High-Quality Clustering. International Conference on Data Engineering, IEEE, pp. 685–694

  30. Özden B, Ramaswamy S, Silberschatz A (1998) Cyclic Association Rules. International Conference on Data Engineering, IEEE, pp. 412–421

  31. Parimala M, Sathiyabama S (2013) Mining sequential pattern with synchronous and asynchronous periodic time stamp using hash based algorithm. J Appl Sci Res 9(4):2602–2609

    Google Scholar 

  32. Rasheed F, Alhajj R (2010) Stnr: a suffix tree based noise resilient algorithm for periodicity detection in time series databases. Appl Intell 32(3):267–278

    Article  Google Scholar 

  33. Richter KF, Schmid F, Laube P (2012) Semantic trajectory compression: representing urban movement in a nutshell. Journal of Spatial Information Science 2012(4):3–30

  34. S Alkoffash M (2012) Automatic arabic text clustering using k-means and k-mediods. Int J Comput Appl 51(2):5–8

    Google Scholar 

  35. Shekhar S, Jiang Z, Ali R, Eftelioglu E, Tang X, Gunturi V et al (2015) Spatiotemporal data mining: a computational perspective. ISPRS International Journal of Geo-Information 4(4):2306–2338

    Article  Google Scholar 

  36. Verhein F (2006) k-STARs: Sequences of Spatio-Temporal Association Rules. IEEE International Conference on Data Mining Workshops, IEEE, pp 387–394

  37. Verhein F (2012) Mining Complex Spatio-Temporal Sequence Patterns. Siam International Conference on Data Mining, DBLP, pp. 605–616

  38. Verhein F, Chawla S (2008) Mining spatio-temporal patterns in object mobility databases. Data Min Knowl Disc 16(1):5–38

    Article  MathSciNet  Google Scholar 

  39. Yang KJ, Hong TP, Chen YM, Lan GC (2013) Projection-based partial periodic pattern mining for event sequences. Expert Syst Appl 40(10):4232–4240

    Article  Google Scholar 

  40. Yang J, Wang W, Yu PS (2000) Mining asynchronous periodic patterns in time series data. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 275–279

  41. Yeh JS, Lin SC (2009) A new data structure for asynchronous periodic pattern mining. International Conference on Ubiquitous Information Management and Communication, ACM, pp. 426–431

  42. Zhang W, Liao H, Zhao N (2009) Research on the FP Growth Algorithm about Association Rule Mining. International Seminar on Business and Information Management, Vol. 1, IEEE, pp 315–318

  43. Zhang SC, Sun XY (2012) Improved clarans algorithm based on grid structure. Comput Eng 38(6):56–59

    Google Scholar 

  44. Zhang Z, Wu W (2015) Composite spatio-temporal co-occurrence pattern mining. Lect Notes Comput Sci 5258:454–465

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by China Postdoctoral Science Foundation (No. 2016 M592697) and Key Science and Technology Project of Shandong Province of China (No. 2014GGH201022).

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Authors and Affiliations

  1. School of Data and Computer Science, Shandong Women’s University, Jinan, China

    Shuxia Dong & Zengzhen Shao

  2. School of Information Science and Engineering, Shandong Normal University, Jinan, China

    Shuxia Dong, Shulei Liu, Yanyu Zhao & Zengzhen Shao

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  1. Shuxia Dong
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  2. Shulei Liu
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  4. Zengzhen Shao
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Correspondence to Shuxia Dong.

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Dong, S., Liu, S., Zhao, Y. et al. An innovative model to mine asynchronous periodic pattern of moving objects. Multimed Tools Appl 78, 8943–8964 (2019). https://doi.org/10.1007/s11042-018-6752-4

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  • DOI: https://doi.org/10.1007/s11042-018-6752-4

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