文章基本信息

标题：An Enhanced k-Means Clustering Algorithm for Pattern Discovery in Healthcare Data
本地全文：下载
作者：Ramzi A. Haraty ; Mohamad Dimishkieh ; Mehedi Masud 等
期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2015
卷号：2015
DOI：10.1155/2015/615740
出版社：Hindawi Publishing Corporation
摘要：The huge amounts of data generated by media sensors in health monitoring systems, by medical diagnosis that produce media (audio, video, image, and text) content, and from health service providers are too complex and voluminous to be processed and analyzed by traditional methods. Data mining approaches offer the methodology and technology to transform these heterogeneous data into meaningful information for decision making. This paper studies data mining applications in healthcare. Mainly, we study k-means clustering algorithms on large datasets and present an enhancement to k-means clustering, which requires k or a lesser number of passes to a dataset. The proposed algorithm, which we call G-means, utilizes a greedy approach to produce the preliminary centroids and then takes k or lesser passes over the dataset to adjust these center points. Our experimental results, which were used in an increasing manner on the same dataset, show that G-means outperforms k-means in terms of entropy and F-scores. The experiments also yield better results for G-means in terms of the coefficient of variance and the execution time.