Adaptive Double Self-organizing Map and its Application in Clustering Gene Expression Data

H. Ressom, D. Wang, P. Natarajan

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations


This paper presents a novel clustering technique known as adaptive double self-organizing map (ADSOM). ADSOM has a flexible topology and performs clustering and cluster visualization simultaneously, thereby requiring no apriori knowledge about the number of clusters. ADSOM combines features of the popular self-organizing map (SOM) with two-dimensional position vectors, which serve as a visualization tool to accurately determine the number of clusters present in the data. ADSOM updates its free parameters during training and it allows convergence of its position vectors to a fairly consistent number of clusters provided that its initial number of nodes is greater than the expected number of clusters. A novel index is introduced based on hierarchical clustering of the final locations of position vectors. The index allows automatic detection of the number of clusters, thereby reducing human error that could be incurred from counting clusters visually. The reliance of ADSOM in identifying the number of clusters is proven by applying it to publicly available yeast gene expression data.

Original languageEnglish (US)
Number of pages6
StatePublished - 2003
Externally publishedYes
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: Jul 20 2003Jul 24 2003


OtherInternational Joint Conference on Neural Networks 2003
Country/TerritoryUnited States
CityPortland, OR

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


Dive into the research topics of 'Adaptive Double Self-organizing Map and its Application in Clustering Gene Expression Data'. Together they form a unique fingerprint.

Cite this