Effects of Semi-supervised Learning on Rough Set-Based C-Means Clustering
| Autor: | Akira Notsu, Katsuhiro Honda, Seiki Ubukata, Takeaki Shimizu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Soft computing
Computer science business.industry 020206 networking & telecommunications Pattern recognition 02 engineering and technology Semi-supervised learning ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering Cluster (physics) 020201 artificial intelligence & image processing Artificial intelligence Rough set business Cluster analysis Semi supervised clustering |
| Zdroj: | iFUZZY |
| DOI: | 10.1109/ifuzzy.2018.8751691 |
| Popis: | As soft computing extensions of hard C-means (HCM) clustering, rough C-means (RCM) and rough set C-means (RSCM), which can deal with positive and possible cluster memberships based on rough set theory, have been proposed and utilized for detecting vague boundaries among clusters. Semi-supervised clustering schemes that utilize information of partial labeled objects are promising approaches for improving the classification performance. In this study, we consider how to introduce semi-supervised clustering schemes to RCM and RSCM. Furthermore, we confirm the effectiveness of the proposed methods through numerical experiments. |
| Databáze: | OpenAIRE |
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