  Big Data is a massive volume of both structured and unstructured data that is
too large and it also difficult to process using traditional techniques.
Clustering algorithms have developed as a powerful learning tool that can
exactly analyze the volume of data that produced by modern applications.
Clustering in data mining is the grouping of a particular set of objects based
on their characteristics. The main aim of clustering is to classified data into
clusters such that objects are grouped in the same clusters when they are
corresponding according to similarities and features mainly. Till now, K-MEANS
is the best utilized calculation connected in a wide scope of zones to
recognize gatherings where cluster separations are a lot than between gathering
separations. Our developed algorithm works with K-MEANS for high quality
clustering during clustering from big data. Our proposed algorithm EG K-MEANS :
Extended Generation K-MEANS solves mainly three issues of K-MEANS: unhealthy
initialization, dynamic centroid selection and empty clustering. It ensures the
best way of preventing unhealthy initialization, dynamic centroid selection and
empty clustering problems for getting high quality clustering.
