Enhanced Clustering Algorithm for Processing Online Data
Ankita Choubey · IOSR Journal of Computer Engineering · 2013
Clustering of data has been of intense need for any organization in the past and various researchers in the field of data mining have been continuously working to find efficient and accurate tools and algorithms for the same.From the proliferation of internet and network applications is pressing the same need more deeply and it is becoming more and more necessary for providing such algorithms by the researchers of data mining.Researchers have been working continuously and finding incremental clustering mechanism to be best suitable for online data.Clustering of very large document databases is useful for both searching and browsing.The Periodic updating of clusters is required due to the dynamic nature of databases.For this purpose incremental clustering is a profitable approach.Incremental clustering algorithm clusters data in dynamic form.It requires initial clusters to be decided in advance i.e. they must be pre exist and fixed.This work proposed a dynamic and novice approach of incremental clustering algorithm for creating efficient clusters and rearrangement of the clusters on the basis of characteristics of the data.Also an approach of retrieval and searching of some specific data from the fixed clusters using a new frequency check method will be compared with the proposed work.