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Clustering is the popular unsupervised learning technique of data mining which divide the data into groups having similar objects and used in various application areas. k-Means is the most popular clustering algorithm among all partition based clustering algorithm to partition a dataset into meaningful patterns. k-Means suffers some shortcomings. This paper addresses two shortcomings of k-Means; pass number of centroids in apriori and does not handle noise. This paper also presents an overview of cluster analysis, clustering algorithms, preprocessing and normalization techniques in modified k-Means to improve the effectiveness and efficiency of the modified k-Means clustering algorithm.