Mining on Medicine Data
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2012, Vol 1, Issue 7
Abstract
In this paper, we propose an approach for Mining as well as data clustering, we have taken some medical data set we not only able to find out the disease detail as well as clustering. Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with low distances among the cluster members, dense areas of the data space, intervals or particular statistical distributions. Most data-mining methods assume data is in the form of a feature-vector (a single relational table) and cannot handle multi-relational data. Two fundamental issues regarding the effectiveness of information gathering from the Web: mismatch and overload. Mismatch means some useful and interesting data has been overlooked, whereas overload means some gathered data is not what users want. Classification and clustering has become an increasingly popular method of multivariate analysis over the past two decades, and with it has come a vast amount of published material. Since there is no journal devoted exclusively to cluster analysis as a general topic and since it has been used in many fields of study. Traditional techniques related to information retrieval (IR) have touched upon the fundamental issues KEYWORDS:- clustering, nearest neighbor, reciprocal nearest neighbor, complete link, probabilistic analysis.
Authors and Affiliations
Manojeet Roy , Prof. Ajay Kushwaha, Reader
Keywords
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