Clustering Gene Expression Data Based on Harmony Search and K-harmonic Means
作者单位：School of Computer Engineering and Science Shanghai University School of Information Science and Engineering Xinjiang University
会议名称：《第11届分布式计算及其应用国际学术研讨会》
会议日期：2014年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)]
基 金：partially supported by Key Project of Science and Technology Commission of Shanghai Municipality [No.10510500600] Shanghai Leading Academic Discipline Project [No.J50103] Innovation Program of Shanghai Municipal Education Commission [No.11YZ03]
关 键 词：KHM HS Clustering Gene expression data Introduction
摘 要：Clustering is one of the most commonly data explorer techniques in Data Mining. K-harmonic means clustering（KHM） is an extension of K-means（KM） and solves the problem of KM initialization using a built-in boosting function. However, it is also suffering from running into local optima. As a stochastic global optimization technique, harmony search（HS） can solve this problem. HS-based KHM, HSKHM not only helps KHM clustering escaping from local optima but also overcomes the shortcoming of slow convergence speed of HS. In this paper, we proposed a hybrid data-clustering algorithm, HSKHM. The experimental results on four real gene expression datasets indicate that HSKHM is superior KHM and HS in most cases. The HSKHM algorithm not only improves the convergence speed of HS but also helps KHM escaping from local optima.