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).
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.