Applying the SAC Algorithm to Extract the Cardiologic Indicators of an Athlete's Level
International Journal of Intelligent Information Systems
Volume 5, Issue 3-1, May 2016, Pages: 23-27
Received: Dec. 19, 2015; Accepted: Dec. 21, 2015; Published: Jun. 18, 2016
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Mohamed Hamlich, Electrical Engineering Department of Hassan II University, ENSAM, Casablanca, Morocco
Mohammed Ramdani, Computer Science Lab of Hassan II University, FSTM, Mohammedia, Morocco
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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.
Ant Colonies, Clustering, Heterogeneous Data, SAC Algorithm, Level Athlete
To cite this article
Mohamed Hamlich, Mohammed Ramdani, Applying the SAC Algorithm to Extract the Cardiologic Indicators of an Athlete's Level, International Journal of Intelligent Information Systems. Special Issue: Smart Applications and Data Analysis for Smart Cities. Vol. 5, No. 3-1, 2016, pp. 23-27. doi: 10.11648/j.ijiis.s.2016050301.13
Copyright © 2016 Authors retain the copyright of this article.
This article 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.
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