Clustering is grouping input data sets into subsets, called 'clusters' within which the elements are somewhat similar. In general, clustering is an unsupervised learning task as very little or no prior knowledge is given except the input data sets. The tasks have been used in many fields and therefore various clustering algorithms have been developed. Clustering task is, however, computationally expensive as many of the algorithms require iterative or recursive procedures and most of real-life

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... ata is high dimensional. Therefore, the parallelization of clustering algorithms is inevitable, and various parallel clustering algorithms have been implemented and applied to many applications. In this paper, we review a variety of clustering algorithms and their parallel versions as well. Although the parallel clustering algorithms have been used for many applications, the clustering tasks are applied as preprocessing steps for parallelization of other algorithms too. Therefore, the applications of parallel clustering algorithms and the clustering algorithms for parallel computations are described in this paper. General Information of Clustering Analysis Jain et al. [41] defined the cluster analysis as the "organization of a collection of patterns into clusters based on similarity". The difficulty lies in the definition and the scope of 'cluster' in the data set. The book [42] lists some of the definitions of a cluster, which are introduced in [27]:1) A cluster is a set of similar objects and the objects in different clusters should not be similar; 2) A cluster is the set of points which are aggregated in the testing environment such that the distance between two points in a cluster is less than the distance between the points of other clusters; 3) Clusters can be densely connected regions in a multidimensional space separated by loosely connected points. Based on the purposed