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The Scientific World Journal
Volume 2014, Article ID 136018, 8 pages
http://dx.doi.org/10.1155/2014/136018
Research Article

Gait Signal Analysis with Similarity Measure

1Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
2Department of Information Security, Tongmyong University, Sinseonno, Nam-gu, Busan 608-711, Republic of Korea

Received 25 April 2014; Accepted 10 June 2014; Published 7 July 2014

Academic Editor: T. O. Ting

Copyright © 2014 Sanghyuk Lee and Seungsoo Shin. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. H. Fisher, “Knowledge acquisition via incremental conceptual clustering,” Machine Learning, vol. 2, no. 2, pp. 139–172, 1987. View at Publisher · View at Google Scholar · View at Scopus
  2. A. K. Jain and R. C. Dubes, Algorithms for Clustering Data, Prentice Hall, 1988. View at MathSciNet
  3. F. Murtagh, “A survey of recent advances in hierarchical clustering algorithms,” Computer Journal, vol. 26, no. 4, pp. 354–359, 1983. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  4. R. S. Michalski and R. E. Stepp, “Learning from observation: conceptual clustering,” in Machine Learning: An Artificial Intelligence Approach, pp. 331–363, Springer, Berlin, Germany, 1983. View at Publisher · View at Google Scholar
  5. H. P. Friedman and J. Rubin, “On some invariant criteria for grouping data,” Journal of the American Statistical Association, vol. 62, no. 320, pp. 1159–1178, 1967. View at Publisher · View at Google Scholar · View at MathSciNet
  6. K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, New York, NY, USA, 1990. View at MathSciNet
  7. “Advancing Discovery in Science and Engineering. Computing Community Consortium,” Spring 2011.
  8. Advancing Personalized Education, Computing Community Consortium, 2011.
  9. S. Lee and T. O. Ting, “The evaluation of data uncertainty and entropy analysis for multiple events,” in Advances in Swarm Intelligence, pp. 175–182, Springer, New York, NY, USA, 2012. View at Google Scholar
  10. S. Park, S. Lee, S. Lee, and T. O. Ting, “Design similarity measure and application to fault detection of lateral directional mode flight system,” in Advances in Swarm Intelligence, vol. 7332 of Lecture Notes in Computer Science, pp. 183–191, Springer, New York, NY, USA, 2012. View at Google Scholar
  11. S. Lee and T. O. Ting, “Uncertainty evaluation via fuzzy entropy for multiple facts,” International Journal of Electronic Commerce, vol. 4, no. 2, pp. 345–354, 2013. View at Google Scholar
  12. S. Lee, W. He, and T. O. Ting, “Study on similarity measure for overlapped and non-overlapped data,” in Proceedings of the International Conference on Information Science and Technology (ICIST '13), pp. 48–53, March 2013.
  13. “Smart Health and Wellbeing,” Computing Community Consortium, 2011.
  14. X. C. Liu, “Entropy, distance measure and similarity measure of fuzzy sets and their relations,” Fuzzy Sets and Systems, vol. 52, no. 3, pp. 305–318, 1992. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. S. Lee, W. Pedrycz, and G. Sohn, “Design of similarity and dissimilarity measures for fuzzy sets on the basis of distance measure,” International Journal of Fuzzy Systems, vol. 11, no. 2, pp. 67–72, 2009. View at Google Scholar · View at MathSciNet · View at Scopus
  16. S. Lee, K. H. Ryu, and G. Sohn, “Study on entropy and similarity measure for fuzzy set,” IEICE Transactions on Information and Systems, vol. 92, no. 9, pp. 1783–1786, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. S. H. Lee, S. J. Kim, and N. Y. Jang, “Design of fuzzy entropy for non convex membership function,” in Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques, vol. 15 of Communications in Computer and Information Science, pp. 55–60, 2008. View at Google Scholar
  18. Y. Cheng and G. Church, “Biclustering of expression data,” in Proceedings of the 8th International Conference on Intelligent System for Molecular Biology, 2000.
  19. D. M. West, Big Data for Education: Data Mining, Data Analytics, and Web Dashboards, Governance Studies at Brookings, Washington, DC, USA, 2012.