A new SOM Initialization Algorithm for Nonvectorial Data

Salvatore Gaglio, Antonino Fiannaca, Alfonso Urso, Salvatore Gaglio, Riccardo Rizzo, Antonino Fiannaca

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Self Organizing Maps (SOMs) are widely used mapping and clustering algorithms family. It is also well known that the performances of the maps in terms of quality of result and learning speed are strongly dependent from the neuron weights initialization. This drawback is common to all the SOM algorithms, and critical for a new SOM algorithm, the Median SOM (M-SOM), developed in order to map datasets characterized by a dissimilarity matrix. In this paper an initialization technique of M-SOM is proposed and compared to the initialization techniques proposed in the original paper. The results show that the proposed initialization technique assures faster learning and better performance in terms of quantization error.
Original languageEnglish
Pages (from-to)41-48
Number of pages8
JournalLECTURE NOTES IN ARTIFICIAL INTELLIGENCE
Volume5177
Publication statusPublished - 2008

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'A new SOM Initialization Algorithm for Nonvectorial Data'. Together they form a unique fingerprint.

  • Cite this

    Gaglio, S., Fiannaca, A., Urso, A., Gaglio, S., Rizzo, R., & Fiannaca, A. (2008). A new SOM Initialization Algorithm for Nonvectorial Data. LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, 5177, 41-48.