Modified K-Means Clustering Algorithm for Disease Prediction

This work presents the outline of K-means clustering algorithm and enhanced technique applied on K-means clustering. The K-means clustering is the basic algorithm to find the groups of data or clusters in the dataset. To find the similar groups of data the initial selection of cetroid is done and the Euclidean distance is calculated from cetroid to all other data points, and based on the smaller Euclidean distance the data points are assigned to that centroid. The initial point selection effects on the results of the algorithm, both in the number of clusters found and their centroids. Methods to enhance the k-means clustering algorithm are discussed. With the help of these methods efficiency, accuracy, performance and computational time are improved. So, to improve the performance of clusters the Normalization which is a pre-processing stage is used to enhance the Euclidean distance by calculating more nearer centers, which result in reduced number of iterations which will reduce the computational time as compared to k-means clustering. By applying this enhanced technique one can build a new proposed algorithm which will be more efficient, supports faster data retrieval from databases, makes the data suitable for analysis and prediction, accurate and less time consuming than previous work.