Neural Network Clustering Based on Distances Between Objects

2006/08/29 by Leonid Litinskii, Litinskii, Leonid B., Dmitry Romanov +1
Computer Science · #Advanced Clustering Algorithms Research #Advanced Computational Techniques in Science and Engineering #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE)

paper · pdf · doi:10.48550/arxiv.cs/0608115

openalex publication_date 2006/08/29 · openalex created_date 2018/03/06 · openalex updated_date 2026/07/28

Abstract

We present an algorithm of clustering of many-dimensional objects, where only the distances between objects are used. Centers of classes are found with the aid of neuron-like procedure with lateral inhibition. The result of clustering does not depend on starting conditions. Our algorithm makes it possible to give an idea about classes that really exist in the empirical data. The results of computer simulations are presented.

Related

• Algorithms and Complexity of Range Clustering
• Neural network-based clustering using pairwise constraints
• Clustering with Neural Network and Index
• Clustering by Constructing Hyper-Planes
• Linear Clustering Process on Networks