ANALYSIS OF VARIOUS CLUSTERING ALGORITHMS OF DATA MINING ON HEALTH INFORMATICS

Abstract

There are large quantities of information about patients and their medical conditions. The discovery of trends and patterns hidden within the data could significantly enhance understanding of disease and medicine progression and management by evaluating stored medical documents. Methods are needed to facilitate discovering the trends and patterns within such large quantities of medical documents. Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Weka is a data mining tools. It contains many machine leaning algorithms. It provides the facility to classify our data through various algorithms. In this paper we are studying the various clustering algorithms. Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more similar (in some sense or another) to each other than to those in other clusters. Our main aim to show the comparison of different clustering algorithms of Data Mining and find out which algorithm will be most suitable for the users working on health data.