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Machine learning is a state-of-the-art sub-project of artificial intelligence, that is been evolved for finding large-scale intelligent analytics in the distributed computing environment. In this paper, we perform comparative analytics onto dataset collected for the electricity usage of home based on the K-mean clustering algorithm using comparison to silhouette score with a ratio of 1/8 dataset. The performance evaluation shows that, the comparison index is similar in numbers of silhouette score even if dataset is smaller than before.

Machine learning is a modern field that has emerged as a new tool for data analytics in the distributed computing environment. There are several aspects, at which, machine learning has improved the processing capacity along with effectiveness of analysis. In this paper, the electricity usage of home is analyzed through K-means clustering algorithm for obtaining the optimal home usage electricity data points. The Davis Boulden Index and Silhouette_score finds the detailed optimal number of clusters in the K-means algorithm and present the application scenario of the machine learning clustering analytics.