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

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 clust...

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Bibliographic Details
Published in:International journal of computers & applications 2022-05, Vol.44 (5), p.465-479
Main Authors: Noureddine, Samia, Zineeddine, Baarir, Toumi, Abida, Betka, Abir, Benharkat, Aïcha-Nabila
Format: Article
Language:English
Subjects:
Biological activity
classification
Clustering
Computer Science
Data mining
Decision analysis
Decision making
Heuristic methods
machine learning
Mathematical analysis
Organisms
Search algorithms
Symbiotic
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Summary: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.
ISSN:1206-212X
1925-7074
DOI:10.1080/1206212X.2020.1809825