A proficient approach for clustering of large categorical data cataloguing
G. Sreenivasulu, S. Viswanadha Raju, N. Sambasiva Rao · 2016
To extract knowledge from Data bases Data mining is being used. Data mining is associated with various techniques. In those Clustering is considered to be one of the best approaches. Clustering a huge data set specifically categorical data is difficult and tedious procedure. In this context a proficient method is proposed that is focused on Rough purity for humanizing accuracy of grouping and keeping the unlabeled objects into proper clusters. Data cataloging is a general method in numerical and mixed domain but it is a key setback in categorical domain. To address this problem we proposed an proficient approach for clustering and cataloguing of categorical data. This system is mainly divided into three parts. Initially data will be divided into various groups, secondly clustering algorithm will be applied and finally Drifting is going to be done when ever complete list of objects are new in a group. This is proven with a large data set in detailed manner. This method improves efficiency for cluster cataloguing.