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 |
Ant Colonies, Clustering, Heterogeneous Data, SAC Algorithm, Level Athlete
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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
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
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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Download@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}
}
TY - JOUR T1 - Applying the SAC Algorithm to Extract the Cardiologic Indicators of an Athlete's Level AU - Mohamed Hamlich AU - Mohammed Ramdani Y1 - 2016/06/18 PY - 2016 N1 - https://doi.org/10.11648/j.ijiis.s.2016050301.13 DO - 10.11648/j.ijiis.s.2016050301.13 T2 - International Journal of Intelligent Information Systems JF - International Journal of Intelligent Information Systems JO - International Journal of Intelligent Information Systems SP - 23 EP - 27 PB - Science Publishing Group SN - 2328-7683 UR - https://doi.org/10.11648/j.ijiis.s.2016050301.13 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. VL - 5 IS - 3-1 ER -
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