Abstract
Today we are surrounded with large data related to health reports of patients. In this paper we will introduce a methodology to extract the useful information (pattern) from raw data by using different unsupervised learning techniques. These hidden patterns will help the practitioner to understand the hidden relation (dependency) among the data. With the help of useful clustering we can predict the hidden trends in patients. We will use the correlation matrix followed by K-mean (fast) to extract the interesting pattern as well as patient state that will help the practitioner to treat the patient wisely. According to the nature of data we can categorize the heart patient into normal, moderate, risk and critical patients. We use the different clustering algorithm and analyze the performance of each algorithm in cardiac dataset. For this research we have used the real dataset provided by AFIC (Armed force institute of cardiology). Data set consist of 1500 records along with 36 attributes. (C) 2016 Published by Elsevier B.V.