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The main aim of any clustering technique is to divide n no. of patterns into some of the clusters in a search space. Literature survey (Jain et. al, 1999; Hruschka et. al., 2009; Hasan and Ramakrishnan, 2011) reveals that different clustering techniques have been developed in recent years. Some descent types of clustering methods those have been frequently used by the researchers are: a) Partition based, b) Hierarchy based, c) Density based, d) Grid based. Basically, the clustering algorithms are based on some criterion to assess the quality of cluster partition. In particular, depending on the parameters, the algorithms receive the inputs as the no. of clusters, cluster density, etc. and find the good partition. K-means (MacQueen, 1967) survey is a partition based hard clustering method. It is a center based algorithm where the Euclidian distance from each point to the center is computed and the aim is to calculate the nearest cluster center. In recent days, several attempts (Arbelaitz et. al., 2013; Roy et. al., 2012; Vanfleteren et. al, 2013; Michie et. al, 2013; Arumugam et. al., 2011; Zhang et. al., 2013; Parmar and Pandi, 2015; Ward et. al., 2015; Wang et. al., 2015; Nguyen et. al., 2015; Cosby et. al., 2015; Boersma et. al., 2014; Jose et. al., 2014; Chen et. al., 2014; Bose et. al.,2013) have been made by using this method and hybridizing it with various optimization techniques to solve diversified problems. Basically, K-means algorithm is more famous than some other clustering algorithms due its simple execution steps with a minimal parameter settings and ablility to run quickly for large datasets. After consideration of a no. of successful applications of K-means, some researchers found some limitations (Arthur and Vassilvitskii, 2007; Singh et. al., 2011; Komali et. al., 2015; Acharya et. al., 2015; Liang et. al., 2015) about the algorithm which need to be addressed. As the algorithm is based on some initial selection of random cluster centers, so after the completion of each iteration, it produces different results. Although, the algorithm is able to minimize the intra cluster variance, but it is ineffective in producing global minimum variance. In a dense clustering environment, the data set is divided into k no. of clusters. For each data, the closest center point is computed based on the distance calculation procedure. But for a cluster having none of the assigning data points, it will result in an empty cluster. Also, for the data set having some outliers, the algorithm is unable to calculate an accurate cluster centroid as it originally would have been. Apart from these problems, the biggest deal is to find the global solution as K-means is highly sensitive to local optima due to the fixed unchangeable cluster centers after some point of calculations of the algorithm. However, a no. of improved versions (Cheung, 2003; Charalampidis, 2005; Şerban and Moldovan, 2006; Zhang et. al., 2008; Celebi, 2011; Tarpey, 2012; Ma et. al., 2015; Shao et. al., 2015; Duwairi and Abu-Rahmeh, 2015) and hybridization algorithm of K-means (Ahmadyfard and Modares, 2008; Wu, 2008; Kao and Lee, 2009; Schreiber, 1991; Gibou amd Fedkiw, 2005; Li et. al., 2015; Aparna and Nair, 2014; Pan et. al., 2014) have been developed. Somehow, these improved algorithms performed well for some particular applications, still they failed to obtain a global solution which is our area of focus.