Clustering in data analysis means data with similar features are grouped together within a particular valid cluster. Each cluster consists of data that are more similar among themselves and dissimilar to data of other clusters. Clustering can be viewed as an unsupervised learning concept from machine learning perspective. In this paper, we have proposed an effective method to obtain better clustering with much reduced complexity. We have evaluated the performances of the classical K-Means approach of data clustering and the proposed Far Efficient K-Means method. The accuracy of both these algorithms were examined taking several data sets taken from UCI [13] repository of machine learning databases. Their clustering efficiency has been compared in conjunction with two typical cluster validity indices, namely the Davies-Bouldin Index and the Dunn's Index for different number of clusters, and our experimental results demonstrated that the quality of clustering by proposed method is much efficient than K-Means algorithm when larger data sets with more number of attributes are taken into consideration.

At present majority of research is on cluster analysis which is based on information retrieval from data that portrays the objects and their association among them. When there is a talk on good cluster formation, then selection of an optimal cluster core or center is the necessary criteria. This is because an inefficient center may result in unpredicted outcomes. Hence, a sincere attempt had been made to offer few suggestions for discovering the near optimal cluster centers. We have looked at few versatile approaches of data clustering like K-Means, TLBOC, FEKM, FECA and MCKM which differs in their initial center selection procedure. They have been implemented on diverse data sets and their inter and intra cluster formation efficiency were tested using different validity indices. The clustering accuracy was also conducted using Rand index criteria. All the algorithms computational complexity was analyzed and finally their computation time was also recorded. As expected, mostly FECA and to some extend FEKM and MCKM confers better clustering results as compared to K-Means and TLBOC as the former ones manages to obtain near optimal cluster centers. More specifically, the accuracy percentage of FECA is higher than the other techniques however, it's computational complexity and running time is moderately higher.

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The findings of image segmentation reflects its expansive applications and existence in the field of digital image processing, so it has been addressed by many researchers in numerous disciplines. It has a crucial impact on the overall performance of the intended scheme. The goal of image segmentation is to assign every image pixels into their respective sections that share a common visual characteristic. In this paper, the authors have evaluated the performances of three different clustering algorithms normally used in image segmentation – the typical K-Means, its modified K-Means++ and their proposed Enhanced Clustering method. The idea is to present a brief explanation of the fundamental working principles implicated in these methods. They have analyzed the performance criterion which affects the outcome of segmentation by considering two vital quality measures namely – Structural Content (SC) and Root Mean Square Error (RMSE) as suggested by Jaskirat et al., (2012). Experimental result shows that, the proposed method gives impressive result for the computed values of SC and RMSE as compared to K-Means and K-Means++. In addition to this, the output of segmentation using the Enhanced technique reduces the overall execution time as compared to the other two approaches irrespective of any image size.

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