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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(4): 264-270 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.518

A Non-symmetrical Weighted K-means with Rank-Based Artificial Bee Colony Algorithm for Medical Diagnosis

Jenn-Long Liu and Chung-Chih Li
Abstract—This study proposes a non-symmetrical weighted k-means (NSWKM) clustering algorithm to improve the accuracy of clustering result. The similarity distance of original k-means algorithm is modified by adding weights with a non-symmetrical form to the distance measurement. Namely, different weights for attributes are applied for clusters such that the contribution of attributes can be adjusted adaptively during the clustering process. In this work, the weights are given via an optimization process using a rank-based artificial bee colony (RABC) algorithm. Furthermore, the proposed NSWKM clustering algorithm combined with the RABC, termed NSWKM-RABC herein, is then applied to the medical diagnoses of five data sets of diseases, including breast cancer, cardiac disease, diabetes, liver disease and hepatitis, to evaluate the performance of the proposed algorithm.

Index Terms—Data sets of diseases, medical diagnoses, non-symmetrical weighted k-means clustering algorithm, rank-based artificial bee colony algorithm.

J. L. Liu is with the Department of Information Management, I-Shou University, Kaohsiung, 84001 Taiwan (e-mail: jlliu@isu.edu.tw).
C. C. Li is with the School of Information Technology, Illinois State University, Normal, IL 61790 USA (e-mail: cli2@ilstu.edu).

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Cite: Jenn-Long Liu and Chung-Chih Li, "A Non-symmetrical Weighted K-means with Rank-Based Artificial Bee Colony Algorithm for Medical Diagnosis," International Journal of Machine Learning and Computing vol. 5, no. 4, pp. 264-270, 2015.

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