In clustering tasks, an algorithm groups related features into categories by analyzing similarities between input examples where similar features are clustered and marked using circles around. Clustering uses include but are not limited to the following: search result grouping such as grouping customers, anomaly detection for suspicious pattern finding, text categorization for finding useful pattern in tests, social network analysis for finding coherent groups, data center computing clusters for finding a way to put related computers together, astronomic data analysis for galaxy formation, and real estate data analysis to identify neighborhoods based on similar features. We will show a Spark MLlib-based solution for the last use cases.