Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICPR.2010.173 | ICPR |
Keywords | Field | DocType |
feature weighting,fuzzy set theory,pattern clustering,generalized improved fuzzy partition,vector quantization,automated feature weighting,afdvq algorithm,fuzzy c-means,modified data,fuzzy partitions,generalized improved fuzzy partitions,vector quantisation,fuzzy declustering-based vector quantization,fuzzy declustering,clustering,feature weight,noisy-biased data,noisy data,noise measurement,accuracy,classification algorithms,algorithm design and analysis,clustering algorithms | Standard algorithms,Weighting,Noise measurement,Pattern recognition,Computer science,Fuzzy logic,Fuzzy set,Vector quantization,Artificial intelligence,Statistical classification,Cluster analysis | Conference |
ISSN | ISBN | Citations |
1051-4651 | 978-1-4244-7542-1 | 2 |
PageRank | References | Authors |
0.40 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Theam Foo Ng | 1 | 41 | 4.86 |
| Tuan Pham | 2 | 503 | 73.75 |
| Changming Sun | 3 | 895 | 88.21 |