Total views : 191

An Enhanced Tree Mining Algorithm for Finding Maximal Periodic Movements from Spatiotemporal Databases

Affiliations

  • Department of IT, SVEC, Tirupati – 517102, Andhra Pradesh, India
  • SVUCE, Tirupati - 517502, Andhra Pradesh, India

Abstract


Objectives: To find effective periodic patterns throughthe symbolic database representation of spatiotemporal data by using an efficient algorithm, ETMA (Enhanced Tree-based Mining Algorithm for large Databases). Methods/Statistical Analysis: There are distinct types of notions used to store and manage transactions data horizontally such as segment, sequence and individual symbols. The proposed algorithm can mine periodic patterns in full-series and subsection series also.The proposed algorithm based on prefix and suffix tree concurrently discover signal, series and section periodicity furthermore recognize and report only the efficient and non-redundant periods through the pruning techniques to abolish redundant (repetitive) periods. Findings: This algorithm finds interesting results with the help of a mixture of testing have been performed to validate the peroformance, strength, scalability, and correctness of the produced results in comparison with traditional algorithms. It is used to identify the three distinctcategories of maximal patterns effectively on various synthetic and reallife datasets. All tests are completedon distinct types of noisy such as insertion, deletion and replacement. ETMA reduces the running time and buildsservice of the proficient symbolic process. Moreover, ETMA simply report time-series instances dynamically, interms of symbol, sequence and segment approaches respectively. The length of the pattern, and proving efficiency of the pruning and searching strategies from synthetic and real datasets is a really open & challenging mining problem. Application/Improvements: This algorithm is 50% better than MAFIA and 20% better than STNR algorithms on Accident dataset and 50% better than STNR and 74% better than ECLAT algorithms on Mushroom dataset.

Keywords

Periodic Patterns, Spatioitemporal Databases, Symbol Periodicity, Segement Periodicity, Sequence Periodicity.

Full Text:

 |  (PDF views: 218)

References


  • Agrawal R, Srikant R. Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conference Santiago, Chile; 1994. p. 1–32.
  • Agrawal R, Imielinski T A, Swami S. Mining association rules between sets of items in large databases. Proceedings of the ACM SIGMOD Conference on Management of data, Washington, D.C.; 1993. p. 1–10.
  • Houtsma M, Swami A. Set-oriented mining of association rules. Research Report IBM Almaden Research Center, San Jose, California, 1993, p. 1–10.
  • Agrawal R, Srikant R. Mining sequential patterns. Proceedings of the International Conference on Data Engineering (ICDE), USA; 1995.
  • Roberto J, Bayardo B. Efficiently mining long patterns from databases. Proceedings of the ACM-SIGMOD International Conference on Management of Data, USA; 1998. p. 85–93.
  • Minos NG, Rajeev , Shim K. SPIRIT Sequential pattern mining with regular expression constraints. Proceedings of the 25th VLDB Conference, Edinburgh, Scotland; 1999. p. 223–34.
  • Pei J, Han H, Mao R. CLOSET: An efficient algorithm for mining frequent closed itemsets. Proceedings of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Dallas, TX, Canada; 2000. p. 1–10.
  • Bastide Y , Rafik T, Nicolas P, Stumme G, Lakhal L. Mining frequent patterns with counting inference, Proceedings of ACM SIGKDD, USA. 2000; 2(2):66–75.
  • Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation, SIGMOD Rec., USA; 2000. p. 1–12.
  • Burdick D, Calimlim M, Gehrke J. MAFIA: A maximal frequent itemset algorithm for transactional databases. International conference on Data Engineering (ICDE), USA; 2001. p. 1–10.
  • Tsoukatos I, Gunopulos D. Efficient mining of spatiotemporal patterns, Lecture Notes in Computer ScienceSpringer-Verlag Berlin Heidelberg. 2001; 2121:425–42.
  • Mohammed J, Hsiao ZC-J. CHARM: An efficient algorithm for closed itemset mining. 2nd {SIAM} International Conference on Data Mining; 2002. p. 1–17.
  • Cheung W, Osmar R, Zaïane Z. Incremental mining of frequent patterns without candidate generation or support constraint. Proceedings of the 7th International Conference on Database Engineering and Applications, Canada; 2003. p. 111–16.
  • Mohammed J, Zaki Z. SPADE: An efficient algorithm for mining frequent sequences. Machine Learning Journal. 2001; 42:31–60 .
  • Lin MY, Lee SY. Fast discovery of sequential patterns through memory indexing and database partitioning. Journal of Information Science and Engineering. 2002; 21(1):109–28 .
  • Wang J, Han J, Pei J. CLOSET+: Searching for the best strategies for mining frequent closed itemsets. Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Washington, D.C; 2003. p. 1–10.
  • Liu G, Lu H, Yu JX. CFP-tree: A compact disk-based structure for storing and querying frequent itemsets. Journal of Information Systems. 2007; 32(2):295–319.
  • Wang J, Han J, Lu Y, Tzvetkov Y. TFP: An efficient algorithm for mining top-K frequent closed itemsets. IEEE Transactions on Knowledge and Data Engineering. 2005; 17(5):652–64 .
  • Cao H, Mamoulis N, Cheung DW. Mining frequent spatio-temporal sequential patterns. Proceedings of the 5th IEEE International Conference on Data Mining (ICDM), Texas, China; 2005. p. 82–9.
  • Cao H, Mamoulis N, Cheung DW. Discovery of periodic patterns spatiotemporal sequences. IEEE Transactions on Knowledge and Data Engineering. 2007; 19(4).
  • Ding ZLB, Han J, Kays R, Nye P. Mining periodic behaviors for moving objects ACMKDD, Washington, DC, USA; 2010. p. 1–10.
  • Kang J, Yong H. Mining spatio-temporal patterns in trajectory data. Journal of Information Processing Systems. 2010; 6(4):521–36.
  • Nizar R, Mabroukeh CI, Ezeife E. A taxonomy of sequential pattern mining algorithms. ACM Computing Surveys. 2010; 43(1):1–4.
  • Li Z, Ji M, Lee JG, Kays R. MoveMine: Mining moving object databases. Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD Indianapolis, Indiana, USA; 2010. p. 1–4.
  • Mohan P, Shekhar S, James A, Shine S, James P, Rogers R. Cascading spatio-temporal pattern discovery. IEEE Transactions on Knowledge and Data Engineering. 2012; 24(11):1977–92.
  • Phuong TD, Thanh DV, Dung ND. An effective algorithm for association rules mining from temporal quantitative databases. Indian Journal of Science and Technology. 2016 May; 9(17):1–8.
  • Obulesu O, Reddy ARM. Fast and efficient frequent and maximal periodic pattern mining in spatiotemporal databases. 6th IEEE International Advanced Computing Conference, Bhimavarm, A.P., India, 2016 Feb; 2016. p. 35–9.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.