@inproceedings{c15b7654db924e3594d95f91ad1a048f,
title = "K-dynamical self organizing maps",
abstract = "Neural maps are a very popular class of unsupervised neural networks that project high-dimensional data of the input space onto a neuron position in a low-dimensional output space grid. It is desirable that the projection effectively preserves the structure of the data. In this paper we present a hybrid model called K-Dynamical Self Organizing Maps (KDSOM) consisting of K Self Organizing Maps with the capability of growing and interacting with each other. The input space is soft partitioned by the lattice maps. The KDSOM automatically finds its structure and learns the topology of the input space clusters. We apply our KDSOM model to three examples, two of which involve real world data obtained from a site containing benchmark data sets.",
keywords = "Artificial Neural Networks, Clustering Algorithms, Self Organizing Maps",
author = "Carolina Saavedra and Hector Allende and Sebasti{\'a}n Moreno and Rodrigo Salas",
year = "2005",
doi = "10.1007/11579427\_71",
language = "English",
isbn = "3540298967",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "702--711",
booktitle = "MICAI 2005",
note = "4th Mexican International Conference on Artificial Intelligence, MICAI 2005 ; Conference date: 14-11-2005 Through 18-11-2005",
}