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Clustering of tourist routes for individual tourists using sequential pattern mining

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Abstract

Grouping individual tourists who have the same or similar tourist routes over the same time period makes it more convenient for the tourists at a low cost by providing transportation means such as regular or occasional tour buses, driver, and tourism guides. In this paper, we propose a mathematical formulation for the tour routes clustering problem and two phases for a sequential pattern algorithm for clustering similar or identical routes according to the tourist routes of individual tourists, with illustrative examples. The first phase is to construct a site by site frequency matrix and prune infrequent tour route patterns from the matrix. The second phase is to perform clustering of the tour routes to determine the tour route using a sequential pattern mining algorithm. We compare and evaluate the performance of our algorithms, i.e., in terms of execution time and memory used. The proposed algorithm is efficient in both runtime and memory usage for the increasing number of transactions.

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References

  1. Ban J (2013) Sustainable tourism marketing strategies for Chinese tourists. The Seoul Institute 2012-PR−65, pp 1–165

  2. Mohamed SJ (2019) Uber: advantages and disadvantages, budgeting and savings investopedia. https://www.investopedia.com/articles/investing/110614/taxi-industry-pros-cons-uber-and-other-ehail-apps.asp. Accessed 20 July 2019

  3. Deitch R, Ladany SP (2001) Determination of optimal one-period tourist bus tours with identical starting and terminal points. Int J Serv Technol Manag 2:116–129

    Google Scholar 

  4. Vansteenwegen P, Souffriau W, Oudheusden DV (2011) The orienteering problem: a survey. Eur J Oper Res 209:1–10

    MathSciNet  MATH  Google Scholar 

  5. Tsiligirides T (1984) Heuristic methods applied to orienteering. J Oper Res Soc 35:797–809

    Google Scholar 

  6. Golden BL, Levy L, Vohra R (1987) The orienteering problem. Nav Res Logist 34:307–318

    MATH  Google Scholar 

  7. Golden BL, Wang Q, Lin L (1988) A multifaceted heuristic for the orienteering problem. Nav Res Logist 35:359–366

    MATH  Google Scholar 

  8. Kantor MG, Rosenwein MB (1992) The orienteering problem with time windows. J Oper Res Soc 43:629–635

    MATH  Google Scholar 

  9. Wang W, Sun X, Golden BL, Jia J (1995) Using artificial neural networks to solve the orienteering problem. Ann Oper Res 61:111–120

    MATH  Google Scholar 

  10. Ramesh R, Yong-Seok Y, Karwan MH (1992) An optimal algorithm for the orienteering problem. ORSA J Comput 4:155–165

    MATH  Google Scholar 

  11. Brilhante IR, Macedo JA, Nardini FM, Perego R, Renso C (2015) On planning sightseeing tours with TripBuilder. Inf Process Manag 51:1–15

    Google Scholar 

  12. Gavalas D, Kasapakis V, Konstantopoulos C, Pantziou Q, Vathis N, Zaroliagis C (2015) The eCOMPASS multimodal tourist tour planner. Expert Syst Appl 42:7303–7316

    Google Scholar 

  13. Majid A, Chen L, Mirza HT, Hussain I, Chen G (2015) A system for mining interesting tourist locations and travel sequences from public geo-tagged photos. Data Knowl Eng 95:66–86

    Google Scholar 

  14. Chen D, Shen X, Karnawat A, Muthiah AS, Farooqui S (2014) An effective approach for mass transit routing and optimization. Contemp Eng Sci 7:405–417

    Google Scholar 

  15. Srikant R, Agrawal R (1996) Mining sequential patterns: generalizations and performance improvements. In: Lecture Notes in Computer Science, vol 1057, pp 1–17

  16. Wang J, Han J, Li C (2007) Frequent closed sequence mining without candidate maintenance. IEEE Trans Knowl Data Eng 19(8):1042–1056

    Google Scholar 

  17. Pokou JM, Fournier-Viger, Moghrabi PC (2016) Authorship attribution using small sets of frequent part-of-speech skip-grams. In: Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference. pp 86–91

  18. Schweizer DM, Zehnder H, Wache HF (2015) Using consumer behavior data to reduce energy consumption in smart homes. In: IEEE International Conference on Machine Learning and Applications. pp 1123–1129

  19. Fournier-Viger P, Nkambou R, Nguifo EM (2008) A Knowledge discovery framework for learning task models from user interactions in intelligent tutoring systems. In: Lecture Notes in Computer Science, vol 5317, pp 765–778

  20. Fournier-Viger P, Gueniche T, Tseng VS (2012) Using partially-ordered sequential rules to generate more accurate sequence prediction. In: Lecture Notes in Computer Science, vol 7713, pp 431–442

  21. Ziebarth S, Chounta I, Hoppe H (2015) Resource access patterns in exam preparation activities. In: Lecture Notes in Computer Science, vol 9307, pp 497–502

  22. Kinnebrew JS, Loretz KM, Biswas G (2013) A contextualized, differential sequence mining method to derive students’ learning behavior patterns. J Educ Data Min 5(1):190–219

    Google Scholar 

  23. Bhatt C, Cooper M, Zhao J (2018) SeqSense: video recommendation using topic sequence mining. In: Lecture Notes in Computer Science, vol 10705, pp 252–263

  24. D’Andreagiovanni M, Baiard F, Lipilini J, Ruggieri S, Tonelli F (2018) Sequential pattern mining for ICT risk assessment and prevention. In: Lecture Notes in Computer Science, vol 10729, pp 25–39

  25. Zaki MJ (2001) SPADE: an efficient algorithm for mining frequent sequences. Mach Learn 42(1–2):31–60

    MATH  Google Scholar 

  26. Pei J, Han J, Mortazavi-Asl B, Wang J, Pinto H, Chen Q, Dayal U, Hsu MC (2004) Mining sequential patterns by pattern-growth: the prefix span approach. IEEE Trans Knowl Data Eng 16(11):1424–1440

    Google Scholar 

  27. Ayres J, Flannick J, Gehrke J, Yiu T (2002) Sequential pattern mining using a bitmap representation. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp 429–435

  28. Yang Z, Kitsuregawa M (2005) LAPIN-SPAM: an improved algorithm for mining sequential pattern. In: Proceeding of 21st International Conference on Data Engineering Workshops. pp 1222–1222

  29. Fournier-Viger P, Gomariz A, Campos M, Thomas R (2014) Fast vertical mining of sequential patterns using co-occurrence information. Lecture Notes in Computer Science. vol 8443, pp 40–52

  30. Xifeng Y, Jiawei H, Afshar R (2003) CloSpan: mining closed sequential patterns in large data base. In: Proceeding of the SIAM International Conference on Data Mining. pp 166–177

  31. Wang J, Han J (2004) BIDE: efficient mining of frequent closed sequences. In: ICDE ‘04 Proceedings of the 20th International Conference on Data Engineering. pp 79–90

  32. Fournier-Viger P, Wu C-W, Tseng VS (2013) Mining maximal sequential patterns without P. Maintenance. In: The International Conference on Advanced Data Mining and Applications. pp 169–180

  33. Gomariz A, Campos M, Marin R, Goethals B (2003) ClaSP: an efficient algorithm for mining frequent closed sequences. Lecture Notes in Computer Science. vol 7818, pp 50–61

  34. Van HTH, Chau VN, Phung NH (2007) An expanded prefix tree-based mining algorithm for sequential pattern maintenance with deletions. In: Proceedings of 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE). pp 11–16

  35. Fournier-Viger P, Lin JCW, Kiran RU, Koh YS (2017) A survey of sequential pattern mining. Data Sci Pattern Recognit 1(1):54–77

    Google Scholar 

  36. Souri A, Hosseini R (2018) A state-of-the-art survey of malware detection approaches using data mining techniques. Hum Cent Comput Inf Sci 8(1):3. https://doi.org/10.1186/s13673-018-0125-x

    Google Scholar 

  37. Fournier-Viger P, Gomariz A, Gueniche T, Soltani A, Wu C-W, Tseng VS (2014) SPMF: a Java open-source pattern mining library. J Machine Learn Res 15:3569–3573

    MATH  Google Scholar 

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Acknowledgements

This research was supported by Soongsil University.

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Authors and Affiliations

  1. Department of Industrial and Information Systems Engineering, Soongsil University, 369 Sangdoro Dongjakku, Seoul, 06978, South Korea

    Gun Ho Lee & Hee Seon Han

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  1. Gun Ho Lee
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  2. Hee Seon Han
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Correspondence to Gun Ho Lee.

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Lee, G.H., Han, H.S. Clustering of tourist routes for individual tourists using sequential pattern mining. J Supercomput 76, 5364–5381 (2020). https://doi.org/10.1007/s11227-019-03010-5

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