期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2019
卷号：7
期号：4
页码：2324-2329
DOI：10.15680/IJIRCCE.2019. 0704021
出版社：S&S Publications
摘要：In order to extract knowledge and patterns in large datasets, data mining can be used. The data mining
tools can work and analyze different types of datasets irrespective of being structured or unstructured. In this work, the
k-means clustering algorithm and SVM (support vector machine) classifier based prediction analysis technique is used
for clustering and classification of the input data. In order to increase the accuracy of prediction analysis, the back
propagation algorithm is proposed to be applied with the k-means clustering algorithm to cluster the data. The proposed
algorithm performance is tested in the heart disease dataset which is taken from UCI repository. There are 76 attributes
present within a database. However, a subset of 14 amongst them is required within all the published experiments.
Specifically, machine learning researchers have used Cleveland database particularly at all times. The proposed work
will also be compared with the existing scheme (using arithmetic mean) in terms of accuracy, fault detection rate and
execution time.
