Automated feature weighting in fuzzy declustering-based vector quantization
2010 (English)Conference paper (Refereed)Text
Feature weighting plays an important role in improving the performance of clustering technique. We propose an automated feature weighting in fuzzy declustering-based vector quantization (FDVQ), namely AFDVQ algorithm, for enhancing effectiveness and efficiency in classification. The proposed AFDVQ imposes weights on the modified fuzzy c-means (FCM) so that it can automatically calculate feature weights based on their degrees of importance rather than treating them equally. Moreover, the extension of FDVQ and AFDVQ algorithms based on generalized improved fuzzy partitions (GIFP), known as GIFP-FDVQ and GIFP-AFDVQ respectively, are proposed. The experimental results on real data (original and noisy data) and modified data (biased and noisy-biased data) have demonstrated that the proposed algorithms outperformed standard algorithms in classifying clusters especially for biased data.
Place, publisher, year, edition, pages
2010. 686-689 p.
, Pattern Recognition (ICPR), 2010 20th International Conference on, ISSN 1051-4651
Computer Vision and Robotics (Autonomous Systems)
IdentifiersURN: urn:nbn:se:liu:diva-127900DOI: 10.1109/ICPR.2010.173ISBN: 978-1-4244-7542-1OAI: oai:DiVA.org:liu-127900DiVA: diva2:928902
2010 International Conference on Pattern Recognition. 23-26 Aug. 2010 Istanbul