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Grade-Based Spatio-Temporal Sequential Pattern Mining using Support and Event Index Measures

Affiliations

  • Department of CSE, Sree Vidyanikethan Engineering College, A. Rangampet, Tirupati – 517102, Andhra Pradesh, India

Abstract


Objectives: The knowledge on cause-effect relationships between instances of real-world entities can be gathered by extracting sequential patterns from spatio-temporal databases. The discovery of the patterns in the context of space and time is a challenging issue. The sequential pattern mining algorithms designed for traditional databases may result in the loss of spatio-temporal correlations due to the improper estimations of properties related to the time and space. The proposed work approaches the problem of designing sequential pattern mining algorithm specifically for spatio-temporal event datasets. Methods/Statistical Analysis: An algorithm is proposed which is based on frequency-based measures for mining frequent spatio-temporal sequential patterns. The spatio-temporal sequential pattern mining based on Support index and Event index algorithm proposes two new parameters support index and event index which are used to scrutinize the sequences extracted from the database. A data structure is also proposed to represent the spatio-temporal data for efficient pattern mining. Findings: The proposed algorithm generates the interesting set of frequent sequential patterns. The proposed algorithm is compared with Slicing-STS-Miner and MST-ITP and the experimental results proved that the proposed algorithm performs well with the order of two to three. Application/Improvements: The proposed algorithm uses frequency-based measures rather than density-based measures. Frequency-based measures take less computational time when compared to density-based measures. The proposed technique is suitable for extracting knowledge in the form of sequential patterns from spatio-temporal point databases.

Keywords

Event Databases, Frequent Pattern, Interestingness Measures, Sequential Pattern, Spatio-Temporal.

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References


  • Verhein F. Mining complex spatio-temporal sequence patterns. Proceedings of 2009 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics; 2009 Apr. p. 605–16. Crossref
  • Cao H, Mamoulis N, Cheung DW. Mining frequent spatiotemporal sequential patterns. Proceedings of Fifth IEEE International Conference on Data Mining; 2005 Nov. p. 82–9. PMid: 15628913.
  • Roddick JF, Spiliopoulou M. A survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and Data Engineering. 2002 Jul; 14(4):750– 67. Crossref
  • Obulesu O, Reddy ARM. An enhanced tree mining algorithm for finding maximal periodic movements from spatio-temporal databases. Indian Journal of Science and Technology. 2016 Nov; 9(41):1–8. Crossref
  • Agrawal R, Srikant R. Mining sequential patterns. Proceedings of International Conference on Data Engineering (ICDE’95); Washington. 1995 Mar. p. 3–14. Crossref
  • Huang Y, Shekhar S, Xiong H. Discovering colocation patterns from spatial data sets: A general approach. IEEE Transactions on Knowledge and Data Engineering. 2004 Dec; 16(12):1472–85. Crossref
  • Tsoukatos I, Gunopulos D. Efficient mining of spatio-temporal patterns. Proceedings of International Symposium on Spatial and Temporal Databases; Springer Berlin Heidelberg. 2001 Jul. p. 425–42. Crossref
  • Mamoulis N, Cao H, Kollios G, Hadjieleftheriou M, Tao Y, Cheung DW. Mining, indexing and querying historical spatio-temporal data. Proceedings of Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ‘04); ACM, New York, NY, USA. 2004 Aug. p. 236–45. Crossref
  • Li Y, Bailey J, Kulik L, Pei J. Mining probabilistic frequent spatio-temporal sequential patterns with gap constraints from uncertain databases. Proceedings of IEEE 13th International Conference on Data Mining (ICDM); 2013 Dec. p. 448–57. Crossref
  • Yun U, Legget JJ. WSPAN. Weighted Sequential Pattern mining in large sequence databases. Proceedings of 3rd International IEEE Conference on Intelligent Systems; 2006 Sep. p. 512–7. Crossref
  • Yun U. WIS, Weighted Interesting Sequential pattern mining with a similar level of support and/or weight. ETRI Journal. 2007; 29(3):336–52. Crossref
  • Sunitha G, Reddy ARM. WRSP-Miner algorithm for mining weighted sequential patterns from spatio-temporal databases. Advances in Intelligent Systems and Computing (AISC). Springer. 2015; 379:309–17.
  • Sunitha G, Reddy ARM. A region-based framework for mining sequential patterns from spatio-temporal event databases. International Journal of Applied Engineering Research. 2014; 9(24):28161–75.
  • Tzouramanis T, Vassilakopoulos M, Manolopoulos Y. Generator for Time-evolving Regional Data (G-TERD). 2013 Feb; 6(3):207–31.
  • Huang Y, Zhang L, Zhang P. A framework for mining sequential patterns from spatio-temporal event data sets. IEEE Transactions on Knowledge and Data Engineering. 2008 Apr; 20(4):433–48. Crossref

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