A new predictive medical approach based on data mining and Symbiotic Organisms Search algorithm
Samia Noureddine, Baarir Zineeddine, Abida Toumi, Abir Betka, Benharkat Aïcha-Nabila · International Journal of Computers and Applications · 2020
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.