In the big data era, the clustering of data or so-called clustering has attracted great interest or attention from researchers in conducting various studies, many grouping algorithms have been proposed in recent times. However, as technology evolves, data volumes continue to grow and data formats are increasingly varied, thus making massive data grouping into a huge and challenging task. To overcome this problem, various research related methods for data grouping have been done, among them is K-Means. However, this method still has some shortcomings, among them is the sensitivity issue in determining the value of cluster (K). In this paper we discuss the implementation of the rule-of-thumb approach and the normalization of data on the K-Means method to determine the number of clusters or K values dynamically in the data groupings. The results show that the implementation of the approach has a significant impact (related to time, number of iterations, and no outliers) in the data grouping
