SVM Classifier on K-means Clustering Algorithm with Normalization in Data Mining for Prediction

Abstract

This work is belonging to K-means clustering algorithms classifier is used with this algorithm to classified data and Min Max normalization technique also used is to enhance the results of this work over simply K- Means algorithm. K-means algorithm is a clustering algorithm and basically used for discovering the cluster within a dataset. Here cancer dataset is used for this research work and dataset is classified in two categories – Cancer and Non-Cancer, after execution of the implemented algorithm with SVM and Normalization technique. The initial point selection effects on the results of the algorithm, both in the number of clusters found and their centroids. In this work enhance the k-means clustering algorithm methods are discussed. This technique helps to improve efficiency, accuracy, performance and computational time. Some enhanced variations improve the efficiency and accuracy of algorithm. The main of all methods is to decrees the number of iterations which will less computational time. K-means algorithm in clustering is most popular technique which is widely used technique in data mining. Various enhancements done on K-mean are collected, so by using these enhancements one can build a new proposed algorithm which will be more efficient, accurate and less time consuming than the previous work. More focus of this studies is to decrease the number of iterations which is less time consuming and second one is to gain more accuracy using normalization technique overall belonging to improve time and accuracy than previous studies.