NBSOM: The naive Bayes self-organizing map

Gonzalo A. Ruz, Duc Truong Pham

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


The naive Bayes model has proven to be a simple yet effective model, which is very popular for pattern recognition applications such as data classification and clustering. This paper explores the possibility of using this model for multidimensional data visualization. To achieve this, a new learning algorithm called naive Bayes self-organizing map (NBSOM) is proposed to enable the naive Bayes model to perform topographic mappings. The training is carried out by means of an online expectation maximization algorithm with a self-organizing principle. The proposed method is compared with principal component analysis, self-organizing maps, and generative topographic mapping on two benchmark data sets and a real-world image processing application. Overall, the results show the effectiveness of NBSOM for multidimensional data visualization.

Original languageEnglish
Pages (from-to)1319-1330
Number of pages12
JournalNeural Computing and Applications
Issue number6
StatePublished - Sep 2012


  • Clustering
  • Data visualization
  • Naive Bayes
  • Online EM algorithm
  • Self-organizing maps
  • Unsupervised learning


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