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Applying the SAC Algorithm to Extract the Cardiologic Indicators of an Athlete's Level

Received: 19 December 2015     Accepted: 21 December 2015     Published: 18 June 2016
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Abstract

The objective of this paper is to identify the parameters that determine the level (high or low) of an athlete. The developed method is based on the algorithms of ant colonies. In this paper We will focus on the application of an algorithm named: SAC “Scout Ant for Clustering”. This method is an extension of existing data clustering algorithms (ACO) based on ant colonies. The clusters’ separation test was improved by using the probabilities determined in step search of the best path between all instances. The SAC method treated any data sets (heterogeneous attributes: continuous and nominal) and represents each cluster by its prototype. This is determined for each cluster and it is the closest instance to all elements of the cluster. This method will be applied to cardiological data, which are taken on athletes.

Published in International Journal of Intelligent Information Systems (Volume 5, Issue 3-1)

This article belongs to the Special Issue Smart Applications and Data Analysis for Smart Cities

DOI 10.11648/j.ijiis.s.2016050301.13
Page(s) 23-27
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Ant Colonies, Clustering, Heterogeneous Data, SAC Algorithm, Level Athlete

References
[1] M. Hamlich, and M. Ramdani, “Data classification by Fuzzy Ant-Miner”, IJCSI International Journal of Computer Sciences issues, Vol 9, Issue 2, N° 3, Marsh 2012, ISSN (Online) 1694-08 14.
[2] M. Hamlich, and M. Ramdani, «Improved ant colony algorithms for data classification», Complex Systems (ICCS), 2012, Agadir, ISBN: 978-1-4673-4764-8, IEE Xplore.
[3] D. Dubois, H. Prade (1996). What are fuzzy rules and how to use them. Fuzzy Sets and Systems, Vol. 84 (2), pp. 169-186.
[4] Y. Kao and K. Cheng, “An ACO-Based Clustering Algorithm”, Tatung University, Taipei, Taiwan (2006).
[5] K. Socha, ACO for Continuous and Mixed-Variable Optimization, Proceedings of the Fourth International workshop on Ant Colony Optimization and Swarm Intelligence, Brussels, Belgium, 2004.
[6] D. Costa and A. Hertz, “Ants Can Colour Graphs.” Journal of the Operational Research Society, 48, 295-305, 1997.
[7] M. Divyavani, T. Amudha, “Comparing the Clustering Efficiency of ACO and K-Harmonic Means Techniques”, International Journal of Computer Science. Engineering and Applications (IJCSEA) Vol. 1, No. 4, August 2011.
[8] M. Hamlich and M. Ramdani, «Applying the Fuzzy Ant-Miner algorithm to extract the success indicators of Balloon Dilation», Congrès International sur les Sciences et Technologies de l’Information et de la Communication (CISTIC2014), Decembre 02-04, 2014.
[9] M. Hamlich and M. Ramdani, “Scout Ants for Clustering”, Journal of Theoretical and Applied Information Technology (JATIT), September 2013 -- Vol. 55. No. 1--2013.
[10] U. Maulik, and S. Bandyopadhyay, (2002) "Performance evaluation of some clustering algorithms and validity indices," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 12.
[11] M. Hamlich and M. Ramdani, “Fuzzy Ant-Miner”, IADIS European Conference Data Mining 2012, Lisboa Portugal, ISBN 978-972-8939-69-4.
[12] G, A. Chan, and A. Freitas, “A new classification-rule pruning procedure for an ant colony algorithm”, Lecture Notes in Artificial Intelligence 2005, 3871 25–36.
[13] RUI XU and DONALD C. WUNSCH, II, “Clustering”, Published by John Wiley & Sons, Inc., Hoboken, New Jersey, 2009 by Institute of Electrical and Electronics Engineers, Library of Congress Cataloging - in-Publication Data is available. ISBN: 978-0-470-27680-8.
Cite This Article
  • APA Style

    Mohamed Hamlich, Mohammed Ramdani. (2016). Applying the SAC Algorithm to Extract the Cardiologic Indicators of an Athlete's Level. International Journal of Intelligent Information Systems, 5(3-1), 23-27. https://doi.org/10.11648/j.ijiis.s.2016050301.13

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    ACS Style

    Mohamed Hamlich; Mohammed Ramdani. Applying the SAC Algorithm to Extract the Cardiologic Indicators of an Athlete's Level. Int. J. Intell. Inf. Syst. 2016, 5(3-1), 23-27. doi: 10.11648/j.ijiis.s.2016050301.13

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    AMA Style

    Mohamed Hamlich, Mohammed Ramdani. Applying the SAC Algorithm to Extract the Cardiologic Indicators of an Athlete's Level. Int J Intell Inf Syst. 2016;5(3-1):23-27. doi: 10.11648/j.ijiis.s.2016050301.13

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  • @article{10.11648/j.ijiis.s.2016050301.13,
      author = {Mohamed Hamlich and Mohammed Ramdani},
      title = {Applying the SAC Algorithm to Extract the Cardiologic Indicators of an Athlete's Level},
      journal = {International Journal of Intelligent Information Systems},
      volume = {5},
      number = {3-1},
      pages = {23-27},
      doi = {10.11648/j.ijiis.s.2016050301.13},
      url = {https://doi.org/10.11648/j.ijiis.s.2016050301.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.s.2016050301.13},
      abstract = {The objective of this paper is to identify the parameters that determine the level (high or low) of an athlete. The developed method is based on the algorithms of ant colonies. In this paper We will focus on the application of an algorithm named: SAC “Scout Ant for Clustering”. This method is an extension of existing data clustering algorithms (ACO) based on ant colonies. The clusters’ separation test was improved by using the probabilities determined in step search of the best path between all instances. The SAC method treated any data sets (heterogeneous attributes: continuous and nominal) and represents each cluster by its prototype. This is determined for each cluster and it is the closest instance to all elements of the cluster. This method will be applied to cardiological data, which are taken on athletes.},
     year = {2016}
    }
    

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    AB  - The objective of this paper is to identify the parameters that determine the level (high or low) of an athlete. The developed method is based on the algorithms of ant colonies. In this paper We will focus on the application of an algorithm named: SAC “Scout Ant for Clustering”. This method is an extension of existing data clustering algorithms (ACO) based on ant colonies. The clusters’ separation test was improved by using the probabilities determined in step search of the best path between all instances. The SAC method treated any data sets (heterogeneous attributes: continuous and nominal) and represents each cluster by its prototype. This is determined for each cluster and it is the closest instance to all elements of the cluster. This method will be applied to cardiological data, which are taken on athletes.
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Author Information
  • Electrical Engineering Department of Hassan II University, ENSAM, Casablanca, Morocco

  • Computer Science Lab of Hassan II University, FSTM, Mohammedia, Morocco

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