An Initialization Method Based on Hybrid Distance for k-Means Algorithm

DOI10.1162/neco_a_01014zbMath1418.62260OpenAlexW2759884981WikidataQ47623209 ScholiaQ47623209MaRDI QIDQ5380869

Xiangfen Zhang, Shunbao Li, Yan Ma, Jie Yang, Yu-Ping Zhang

Publication date: 6 June 2019

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/neco_a_01014

zbMATH Keywords

hybrid distanceclustering validation index based on neighbors (CVN)

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Metric spaces, metrizability (54E35)

Related Items

A multi-stage hierarchical clustering algorithm based on centroid of tree and cut edge constraint ⋮ CciMST: a clustering algorithm based on minimum spanning tree and cluster centers

Uses Software

• k-means++

Cites Work

• An initialization method for the \(K\)-means algorithm using neighborhood model
• Clustering to minimize the maximum intercluster distance
• Initialization of \(K\)-modes clustering using outlier detection techniques
• Data mining and knowledge discovery with evolutionary algorithms
• Data Clustering: Theory, Algorithms, and Applications
• Unnamed Item
• Unnamed Item
• Unnamed Item