Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine

  • Authors

    • D Senthil
    • G Suseendran
    2018-06-08
    https://doi.org/10.14419/ijet.v7i2.33.13890
  • Time Series Data, Sliding Window, Indexing, IARM, ESVM, Segmentation.

  • Abstract

    Time series analysis is an important and complex problem in machine learning and statistics. In the existing system, Support Vector Machine (SVM) and Association Rule Mining (ARM) is introduced to implement the time series data. However it has issues with lower accuracy and higher time complexity. Also it has issue with optimal rules discovery and segmentation on time series data. To avoid the above mentioned issues, in the proposed research Sliding Window Technique based Improved ARM with Enhanced SVM (SWT-IARM with ESVM) is proposed. In the proposed system, the preprocessing is performed using Modified K-Means Clustering (MKMC). The indexing process is done by using R-tree which is used to provide faster results. Segmentation is performed by using SWT and it reduces the cost complexity by optimal segments. Then IARM is applied on efficient rule discovery process by generating the most frequent rules. By using ESVM classification approach, the rules are classified more accurately.

     

     

  • References

    1. [1] Motoyoshi M, Miura T & Watanabe K, “Mining temporal classes from time series dataâ€, Proceedings of the 11th ACM International Conference on Information and Knowledge Management, (2002), pp.493–498.

      [2] Plant C, Wohlschläger AM & Zherdin A, “Interaction-based clustering of multivariate time seriesâ€, Ninth IEEE International Conference on Data Mining, (2009).

      [3] Elfeky MG, Aref WG & Elmagarmid AK, “Periodicity Detection in Time Series Databasesâ€, IEEE Transactions on Knowledge and Data Engineering, Vol.17, (2005), pp.875–887.

      [4] Gorunescu F, Data Mining Concepts, Models and Techniques, Springer, Romania, (2010).

      [5] Feng X & Huang H, “A Fuzzy Set Based Reconstructed Phase Space Method for Identification of Temporal Patterns in Complex Time Seriesâ€, IEEE Transactions on Knowledge and Data Engineering, Vol.17, (2005), pp.601-613.

      [6] Han MK, Data Mining: Concepts and Techniques, Morgan Kaufmann, San Francisco, (2001).

      [7] Hota HS, Handa R & Shrivas AK, “Time Series Data Prediction Using Sliding Window Based RBF Neural Networkâ€, International Journal of Computational Intelligence Research, Vol.13, No.5, (2017), pp.1145-1156.

      [8] Vafaeipour M, Rahbari O, Rosen MA, Fazelpour F & Ansarirad P, “Application of sliding window technique for prediction of wind velocity time seriesâ€, International journal of Energy and environmental engineering (springer), Vol.5,(2014), pp.105-111

      [9] Yin KC, Hsieh YL & Yang DL, “GLFMiner: global and local frequent pattern mining with temporal intervalsâ€, the 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), (2010), pp.2248-2253.

      [10] Sengupta N, Aloka S, Narayanaswamy B, Ismail H & Mathew S, “Time series data mining for demand side decision supportâ€, Innovative Smart Grid Technologies-Asia, (2013), pp.1-6.

      [11] Niennattrakul V & Ratanamahatana CA, “On clustering multimedia time series data using k-means and dynamic time warpingâ€, International Conference on Multimedia and Ubiquitous Engineering, (2007).

      [12] Chandrakala S & Chandra Sekhar C, “A density based method for multivariate time series clustering in kernel feature spaceâ€, IEEE International Joint Conference on Neural Networks, IEEE World Congress on Computational Intelligence, (2008).

      [13] Viet HH & Anh DT, “M-tree as an index structure for time series dataâ€, International Conference on. Computing, Management and Telecommunications, (2013).

      [14] Winarko E & Roddick JF, “A signature-based indexing method for efficient content-based retrieval of relative temporal patternsâ€, IEEE Transactions on Knowledge and Data Engineering, Vol.20, No.6, (2008), pp.825-835.

      [15] Mokbel MF, Xiong X, Hammad MA & Aref WG, “Continuous query processing of spatio-temporal data streams in placeâ€, GeoInformatica, Vol.9, No.4,(2005), pp.343-365.

      [16] Miao R & Shen XJ, “Construction of periodic temporal association rules in data miningâ€, Seventh International Conference on. Fuzzy Systems and Knowledge Discovery (FSKD), (2010).

      [17] Cao L, “Support vector machines experts for time series forecastingâ€, Neuro computing, Vol.51, (2003), pp.321-339.

      [18] Wessa. http://www.wessa.net/stocksdata.wasp, 2011.

      [19] Data Market. http://datamarket.com/, 2011.

  • Downloads

  • How to Cite

    Senthil, D., & Suseendran, G. (2018). Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine. International Journal of Engineering & Technology, 7(2.33), 218-223. https://doi.org/10.14419/ijet.v7i2.33.13890

    Received date: 2018-06-08

    Accepted date: 2018-06-08

    Published date: 2018-06-08