μ-SOM : Weighting features during clustering
type: Dataset
description: Real life datasets used in marketing studies contain a lot of redundant features which may prevent data-mining techniques such as self-organizing maps from discovering relevant clusters. An extension of the batch Kohonen's algorithm is proposed in this paper to avoid the large amount of work which is required by data preprocessing if redundancy isn't treated explicitly by the training method. The proposed approach integrates a weighting of variables built on a simultaneous clustering of both observations and variables and avoids the side effects of redundancy. An application to market segmentation is then briefly described to validate the learning algorithm introduced; identified clusters of products and motivations are used to simplify the analysis of the consumer segmentation by giving the user a first rough description of the different groups.
issued: 2026-05-11T05:20:28.764668
language: en
modified: 2026-05-11T05:20:28.764675
contact point: Organization
  fn: CCSD
keyword: conference-papers
keyword: data-mining
keyword: infoeu-reposemanticsconferenceobject
keyword: infoinfo-lgcomputer-science-csmachine-learning-cslg
keyword: infoinfo-necomputer-science-csneural-and-evolutionary-computing-csne
keyword: market-segmentation
keyword: redundant-features
keyword: self-organizing-maps
keyword: weighting
landing page: Proceedings of the 5th Workshop On Self-Organizing Maps (WSOM'05)

μ-SOM : Weighting features during clustering
type: Distribution
format: HTML
issued: 2026-05-11T05:20:28.776442
modified: 2026-05-11T05:20:28.753788
access URL: https://hal.science/hal-00082061

test_moissonnage_selune
type: Agent