On the impact of dissimilarity measure in k-modes clustering algorithm.
This correspondence describes extensions to the k-modes algorithm for clustering categorical data. By modifying a simple matching dissimilarity measure for categorical objects, a heuristic approach was developed in [4], [12] which allows the use of the k-modes paradigm to obtain a cluster with strong intrasimilarity and to efficiently cluster large categorical data sets. The main aim of this paper is to rigorously derive the updating formula of the k-modes clustering algorithm with the new dissimilarity measure and the convergence of the algorithm under the optimization framework.
Ng
Michael K
MK
Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong. mng@math.hkbu.edu.hk
Li
Mark Junjie
MJ
Huang
Joshua Zhexue
JZ
He
Zengyou
Z
D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D016000:Cluster Analysis; D016247:Information Storage and Retrieval / Q000379:methods*; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity