Premium
Self‐organization of neural networks for clustering
Author(s) -
Maeda Yutaka,
Yotsumoto Yuuichiro,
Kanata Yakichi
Publication year - 1997
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/(sici)1520-6416(199710)121:1<51::aid-eej7>3.0.co;2-z
Subject(s) - artificial neural network , cluster analysis , computer science , artificial intelligence , histogram , usable , sample (material) , data mining , function (biology) , scheme (mathematics) , pattern recognition (psychology) , process (computing) , machine learning , mathematics , image (mathematics) , mathematical analysis , chemistry , chromatography , evolutionary biology , world wide web , biology , operating system
Generally, there are many methods of categorizing unknown data in statistics. In many of these methods, we need sample data to determine the borders of the groups to which these data belong. Neural networks are also usable to classify unknown data. In the learning process of neural networks, we must prepare so‐called teaching signals, that is, sample data. In this paper, we propose an empirical scheme to organize neural networks for clustering unknown data which belong to two groups. In our scheme, a neural network that satisfies an evaluation function without teaching signals is organized. This evaluation function is determined by a histogram of outputs of the neural network. Generally, neural networks map the input data distribution to the output distribution. Maximizing the evaluation function means separating these two output distributions from each other. As an organizing mechanism, the genetic algorithm is used because of its ability to converge to a global maximum. Some numerical results are presented to confirm the feasibility of the scheme. © 1997 Scripta Technica, Inc. Electr Eng Jpn, 121(1): 51–59, 1997