A Theoretical Approach to Optimizing A k-Means Clustering Algorithm In Data Science/Big Data (with a view to Artificial Intelligence)

Applications of Artificial Intelligence have been penetrating deeply into various kinds of domains, at faster rates, such as data science. The general k-means clustering algorithm may not properly deal with larger data sets. So, optimization techniques such as an optimized clustering algorithm for efficient decision makings are necessary to improve the performance efficiency of k-means clustering algorithm further by considering standard deviation and variance of the given data set, to deal with large data sets in data science with a view to Artificial Intelligence.