K-dynamical self organizing maps

Carolina Saavedra, Hector Allende, Sebastián Moreno, Rodrigo Salas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


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.

Original languageEnglish
Title of host publicationMICAI 2005
Subtitle of host publicationAdvances in Artificial Intelligence - 4th Mexican International Conference on Artificial Intelligence, Proceedings
Number of pages10
StatePublished - 2005
Externally publishedYes
Event4th Mexican International Conference on Artificial Intelligence, MICAI 2005 - Monterrey, Mexico
Duration: 14 Nov 200518 Nov 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3789 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference4th Mexican International Conference on Artificial Intelligence, MICAI 2005


  • Artificial Neural Networks
  • Clustering Algorithms
  • Self Organizing Maps


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