  Big Data processing systems handle huge unstructured and structured data to
store, process, and analyze through cluster analysis which helps in identifying
unseen patterns to find the relationships between them. Clustering analysis
over the shared machines in big data technologies helps in deriving the
relations and making decisions using data in context. It can handle every form
of raw, tabular data along with structured, semi-structured, and unstructured
data. The data doesn't have to possess linearity property. It can reflect
associative and correlative patterns and groupings. The main contribution and
findings of this paper are to gather and summarize the recent big data
clustering techniques, and their strengths, and weaknesses in any distributed
environment.
