Reducing the Waiting Time of a Treatment Report by using K-Means Algorithm in Big Data

K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters fixed apriority. The main idea is to define k centers, one for each cluster. These centers should be placed in a cunning way because of different location causes different result. In this research work, proposed algorithm will perform better while handling clusters of circularly distributed data points and slightly overlapped clusters. Big data is a time period used to consult records sets which can be too large or complex for traditional facts-processing utility software program to effectively address. Big information demanding situations include taking pictures statistics, records garage, statistics evaluation, search, sharing, switch, visualization, querying, updating, records private ness and records supply.