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A new predictive medical approach based on data mining and Symbiotic Organisms Search algorithm

Samia Noureddine Baarir Zineeddine Abida Toumi Abir Betka Aïcha-Nabila Benharkat 1
1 SOC - Service Oriented Computing
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Handling very large data, in order to make the best decision, is only possible through an extraction of knowledge. Data mining has become a widely used process in data analytics to extract the most important knowledge for predictive decision making. One of the important types of data mining is clustering mechanism ; its purpose is dividing data into a set of clusters with very large data, the numbers of parameters are very high, and the clustering problem is more difficult. Metaheuristics have been widely used in clustering; they can provide satisfactory solutions for complex problems. The main objective of this paper is to propose a new clustering algorithm based on a metaheuristic technique called Symbiotic Organisms Search (SOS), it was inspired from a biological process, and it simulates the symbiotic interaction between organisms of the same population. The SOS method is used to find the optimal centers of a number of clusters, as a supervised data mining technique. Experimental results have been performed through two phases. Firstly, the SOS technique is benchmarked with six well-known test functions. Secondly, different medical datasets have been used to test our proposed clustering method based on SOS, and show its credibility of treatment. ARTICLE HISTORY
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Submitted on : Wednesday, September 9, 2020 - 11:39:18 AM
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Samia Noureddine, Baarir Zineeddine, Abida Toumi, Abir Betka, Aïcha-Nabila Benharkat. A new predictive medical approach based on data mining and Symbiotic Organisms Search algorithm. International Journal of Computers and Applications, ACTA Press, 2020, pp.1-15. ⟨10.1080/1206212X.2020.1809825⟩. ⟨hal-02929914⟩



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