A survey on partitional clustering using single-objective metaheuristic approach

Clustering is one of the important functions of data mining, which is used to analyse a large amount of data. It groups these set of data according to some similarity property such that data within the cluster are similar to each other and data between the clusters are dissimilar to each other. To obtain an optimal clustering result with the help of an optimisation algorithm is an emerging trend in data mining. The partitional clustering is one of the popularly used types of clustering algorithm. These algorithms often land in local optimum and number of clusters needs to be predefined. To encounter the above problem, optimisation algorithms such as metaheuristic algorithms are used as a suitable problem-solving paradigm. This paper presents an overview of single-objective metaheuristic algorithms used for partitional clustering problem and their applications. This paper even presents the research issues which can be dealt with in future.