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Reseach Article

Fuzzy Decision Tree and Particle Swarm Optimization for Mining of Time Series Data

by Maya Nayak, Satyabrata Dash
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 17 - Number 7
Year of Publication: 2011
Authors: Maya Nayak, Satyabrata Dash
10.5120/2230-2845

Maya Nayak, Satyabrata Dash . Fuzzy Decision Tree and Particle Swarm Optimization for Mining of Time Series Data. International Journal of Computer Applications. 17, 7 ( March 2011), 35-41. DOI=10.5120/2230-2845

@article{ 10.5120/2230-2845,
author = { Maya Nayak, Satyabrata Dash },
title = { Fuzzy Decision Tree and Particle Swarm Optimization for Mining of Time Series Data },
journal = { International Journal of Computer Applications },
issue_date = { March 2011 },
volume = { 17 },
number = { 7 },
month = { March },
year = { 2011 },
issn = { 0975-8887 },
pages = { 35-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume17/number7/2230-2845/ },
doi = { 10.5120/2230-2845 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:04:59.642069+05:30
%A Maya Nayak
%A Satyabrata Dash
%T Fuzzy Decision Tree and Particle Swarm Optimization for Mining of Time Series Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 17
%N 7
%P 35-41
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a new approach for power signal time series data mining using S-transform based K-means clustering technique and fuzzy decision tree. Initially the power signal time series disturbance data are pre-processed through an advanced signal processing tool such as S-transform and various statistical features are extracted, which are used as inputs to the K-means algorithm for disturbance event detection. Particle Swarm Optimization (PSO) technique is used to optimize cluster centers which can be inputs to a fuzzy decision tree for pattern classification of time varying database like the power signal data bases.

References
  1. J. Han, M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, San Francisco, 2001 pp. 346–389.
  2. H. Ralambondrainy, “A conceptual version of the K-means algorithm,” Pattern Recognit. Lett., vol. 16, no. 11, pp. 1147–1157, 1995.
  3. T. Zhang, “Data clustering for very large datasets plus applications,” Ph. D. dissertation, Dept. Comput. Sci., Univ. Wisconsin, Madison, 1997.
  4. Z. Struzik and A. Sibes. Measuring time series similarity through large singular features revealed with wavelet transformation, In Proc. of the 10th Intl. Workshop on Database and Expert Systems Appl., pg. 162–166, 1999.
  5. T.W. Liao, “Clustering time series data – a survey”, Pattern Recognition, vol.38, 2005, pp.1857-1874.
  6. D. Buras et al, Wavelet and neural structure: A new tool for diagnostic of power system disturbances, IEEE Trans. on Industry Applications, 37(1) (2001) 184-190.
  7. W.Lin, C.Wu, Chia-Hung Lin, Fu-Sheng, Detection , classification of multiple power quality disturbances with wavelet multiclass SVM, IEEE Trans. on Power Delivery, 23(4)(2008)2575-2582.
  8. A.M. Gouda, M.M.A. Salama, S.H. Kanoun, and A.Y. Chikhani, Pattern Recognition Applications for Power System Disturbance Classification, IEEE Trans. on Power Delivery, 17(3) (2002) 677-682.
  9. Z.-L. Gaing, Wavelet-based neural network for power disturbance recognition and classification, IEEE Trans. on Power Delivery, 19(4) (2004) 1560–1568.
  10. Haibo He and J. A. Starzyk, A Self-Organizing Learning Array System for Power Quality Classification Based on Wavelet Transform, IEEE Trans. on Power Delivery, 21(1), (2006) 286-295.
  11. I.W.C. Lee, P.K.Dash, “S-transform based intelligent system for classification of power quality disturbance signals”, IEEE Transactions of Industrial Electronics, vol.50, no.4, pp.800-806.
  12. P, K Dash, B.K Panigrahi, and G.Panda “Power Quality Analysis Using S Transform”, IEEE Transaction on Power Delivery, vol.18,No 2,April 2003.
  13. Nicolaos B. Karayiannis, and James C. Bezdek, “An Integrated Approach to Fuzzy Learning Vector Quantization and Fuzzy C-means Clustering ” IEEE Trans. on Fuzzy System, vol. 5, no. 4, November 1997.
  14. R.A.Brown, Richard Frayne, “A Fast Discrete S-Transform for Biomedical Signal Processing”, 30th Annual IEEE EMBS conference, Vancouver, Aug.2008, pp.2586-2589.
  15. Selim, S. Z. and Ismail, M. A., “K-means Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality,” IEEE Trans. Pattern Anal. Mach. Intell. vol. 6, pp.81-87, 1984.
  16. Kennedy, J. and Eberhart, R., “Particle Swarm Optimization,” Proc. of IEEE International Conference on Neural Networks (ICNN), Perth, Australia, vol.4, pp.1942-1948, 1995.
  17. Eberhart, R. and Kennedy, J., “A New Optimizer Using Particle Swarm Theory,” Proc. 6th Int. Symposium on Micro Machine and Human Science, pp. 39-43, 1995.
Index Terms

Computer Science
Information Sciences

Keywords

Time frequency transform S-transform Power signal time series data K-means clustering decision tree