An Overview of Data Mining Techniques and Clustering concepts
International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC).
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This paper provides an overview of Data mining is used to extract meaningful information and to develop significant relationships among variables stored in large data set/data warehouse. In the case study reported in this paper, a data mining approach is applied to extract knowledge from a data set. We present techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. Fast retrieval of the relevant information from databases has always been a significant issue. There are many techniques are developed for this purpose; In among data clustering is one of the major technique. The process of creating vital information from a huge amount of data is learning. It can be classified into two such as supervised learning and unsupervised learning. Clustering is a kind of unsupervised data mining technique. It describes the general working behavior, the methodologies followed by these approaches and the parameters which affect the performance of these algorithms. In classifying web pages, the similarity between web pages is a very important feature. The main objective of this paper is to gather more core concepts and techniques in the large subset of cluster analysis.
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Data mining Clustering, Unsupervised Learning Web Pages, Classifications