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：11
页码：4101-4106
DOI：10.15680/IJIRCCE.2019. 0711002
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.
