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.

In terms of mortality, breast cancer occupies the 5th place in the world. According to the World Health organization (WHO), breast cancer is the principal cause of cancer death in the last years, especially among women. Following this very alarming finding, research was developed using predictive approaches based on the analysis of a large volume of data. In the literature, data analysis was carried out through data mining using several techniques, especially metaheuristics, to aid in the preventive or predictive decision of breast cancer. In this paper, a recent metaheuristic called Symbiotic Organisms Search (SOS) based on a bio-inspired phenomenon is combined with an Artificial Neural Network (ANN), will be utilized in datamining for a predictive study of breast cancer disease. This paper contains also an intelligent application using the SOS algorithm. The experiments generated very confident results in terms of predictive decision following the classification of data.