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Frequent Pattern Mining of Trajectory Coordinates using Apriori Algorithm

by Arthur.A.Shaw, N.P. Gopalan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 22 - Number 9
Year of Publication: 2011
Authors: Arthur.A.Shaw, N.P. Gopalan
10.5120/2615-3094

Arthur.A.Shaw, N.P. Gopalan . Frequent Pattern Mining of Trajectory Coordinates using Apriori Algorithm. International Journal of Computer Applications. 22, 9 ( May 2011), 1-7. DOI=10.5120/2615-3094

@article{ 10.5120/2615-3094,
author = { Arthur.A.Shaw, N.P. Gopalan },
title = { Frequent Pattern Mining of Trajectory Coordinates using Apriori Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { May 2011 },
volume = { 22 },
number = { 9 },
month = { May },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume22/number9/2615-3094/ },
doi = { 10.5120/2615-3094 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:08:54.809512+05:30
%A Arthur.A.Shaw
%A N.P. Gopalan
%T Frequent Pattern Mining of Trajectory Coordinates using Apriori Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 22
%N 9
%P 1-7
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Frequent pattern mining has been an emerging and active field in data mining research for over a decade. Abundant literature has been emerged from this research and tremendous progress has been made in numerous research frontiers. This article, provide an application of the modified Apriori algorithm in coordinate sets of trajectories to find the frequent trajectory coordinates. In this algorithm additional steps are added to prune the coordinate sets generated so that to reduce the unnecessary search time and space. This sequential pattern mining method is quite simple in nature but complex to implement. This paper explains the basics of data origination, database structure to hold the coordinate datasets and the implementation of the algorithm with the object oriented programming language by an illustration. It can be applied to interesting game domains to find the frequent trajectory of an object shot by a player which follows a trajectory path.

References
  1. Arthur.A.Shaw, Mining Frequent Curve Patterns using Apriori Algorithm. In: Proceedings of the International Conference on Innovative Research In Engineering And Technology, ICIRET 2010, Coimbatore, India.
  2. Jiawei Han, Hong Cheng,Dong Xin, Xifeng Yan (2007) Frequent pattern mining: current status and future directions. In the Journal of Data Min Knowl Disc (2007) 15:55–86, Springer Science+Business Media, LLC 2007.
  3. Agrawal R, Imielinski T, and Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993ACM-SIGMOD International conference on management of data (SIGMOD’93), Washington, DC, pp 207–216.
  4. Anthony J.T. Lee, Yi-An Chen, Weng-Chong Ip (2009). Mining frequent trajectory patterns in spatial–temporal databases. In the Journal of Information Sciences 179 (2009) 2218–2231.
  5. United States Patent Application Publication – Baseball Practice Systems, Pub. No.: US2009/0163301 A1, Inventors: John Flading, Marietta, GA (US) and Larry Duan Cripe, Seattle, WA (US).
  6. United States Patent Application Publication – Trajectory Detection And Feedback System For Tennis, Pub. No.: US2008/0200287 A1, Inventors: Marty, Alan W. (Menlo Park, CA, US) and Edwards, Thomas A. (Menlo Park, CA, US).
  7. J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, M.C. Hsu, Mining frequent patterns without candidate generation, in: Proceedings of the ACM SIGMOD International Conference on Management of Data, 2000, pp. 1–12.
  8. T. Hu, S.Y. Sung, H. Xiong, Q. Fu, Discovery of maximum length frequent itemsets, Information Sciences 178 (1) (2008) 69–87.
  9. J.X. Yu, Z. Chong, H. Lu, Z. Zhang, A. Zhou, A false negative approach to mining frequent itemsets from high speed transactional data streams, Information Sciences 176 (14) (2006) 1986–2015.
  10. M.J. Zaki, SPADE: an efficient algorithm for mining frequent sequences, Machine Learning 11(5)(2001)31–60.
  11. J, Ayres, J.E. Gehrke, T. Yiu, J. Flannick, Sequential pattern mining using a bitmap representation, in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002, pp. 429–435.
  12. J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal and M.C. Hsu. FreeSpan: frequent pattern-projected sequential pattern mining, in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000, pp. 355–359.
  13. J. Pei, J. Han, B. Mortazavi-Asl, Q. Chen, U. Dayal and M.C. Hsu. PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth, in: Proceedings of the IEEE International Conference on Data Engineering, 2001, pp. 215–224.
  14. R. Srikant, R. Agrawal, Mining sequential patterns: generalizations and performance improvements, in: Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology, 1996, pp. 3–17.
  15. E. Gudes, E. Shimony, N. Vanetik, Discovering frequent graph patterns using disjoint paths, IEEE Transactions on Knowledge and Data Engineering 18(11) (2006) 1441–1456.
  16. J. Huan, W. Wang, J. Prins, Efficient mining of frequent subgraphs in the presence of isomorphism, in: Proceedings of the IEEE International Conference on Data mining, 2003, pp. 549–552.
  17. Y. Huang, H. Xiong, W. Wu, P. Deng, Z. Zhang, Mining maximal hyperclique pattern: a hybrid search strategy, Information Sciences 177 (3) (2007) 703–721.
  18. A. Inokuchi, T. Washio, H. Motoda, An Apriori-based algorithm for mining frequent substructures from graph data, in: Proceedings of the European Conference on Principles and Practice of Knowledge in Databases, 2000, pp. 13–23.
  19. R. Jin, C. Wang, D. Polshakov, S. Parthasarathy, G. Agarwal, Discovery frequent topological structures from graph datasets, in: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2005, pp. 606–611.
  20. M. Kuramochi, G. Karypis, Frequent subgraph discovery, in: Proceedings of the IEEE International Conference on Data Mining, 2001, pp. 313–320.
  21. X. Yan, J. Han, gSpan: graph-based substructure pattern mining, in: Proceedings of International Conference on Data Mining, 2002, pp. 721–724.
  22. U. Yun, A new framework for detecting weighted sequential patterns in large sequence databases, Knowledge-Based Systems 21 (2) (2008) 110–122.
  23. M. Garofalakis, R. Rastogi, K. Shim, Mining sequential patterns with regular expression constraints, IEEE Transactions on Knowledge and Data Engineering 14(3) (2002) 530–552.
  24. F. Giannotti, M. Nanni, D. Pedreschi, F. Pinelli, Trajectory Pattern Mining, In: Proceedings of the 13th ACM SIGKDD International Conference on KDD’07, USA, pp. 330–339.
  25. Stefano Spaccapietra, Christine Parent, Maria Luisa Damiani, Jose Antonio de Macedo, Fabio Porto and Christelle Vangenot, A conceptual view on trajectories, In: Elsevier Data & Knowledge Engineering 65 (2008) 126-146.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining Association mining Frequent pattern mining trajectory pattern mining

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