Document Detail

An axiomatic approach to soft learning vector quantization and clustering.
MedLine Citation:
PMID:  18252616     Owner:  NLM     Status:  In-Data-Review    
This paper presents an axiomatic approach to soft learning vector quantization (LVQ) and clustering based on reformulation. The reformulation of the fuzzy c-means (FCM) algorithm provides the basis for reformulating entropy-constrained fuzzy clustering (ECFC) algorithms. According to the proposed approach, the development of specific algorithms reduces to the selection of a generator function. Linear generator functions lead to the FCM and fuzzy learning vector quantization algorithms while exponential generator functions lead to ECFC and entropy-constrained learning vector quantization algorithms. The reformulation of LVQ and clustering algorithms also provides the basis for developing uncertainty measures that can identify feature vectors equidistant from all prototypes. These measures are employed by a procedure developed to make soft LVQ and clustering algorithms capable of identifying outliers in the data set. This procedure is evaluated by testing the algorithms generated by linear and exponential generator functions on speech data.
N B Karayiannis
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council     Volume:  10     ISSN:  1045-9227     ISO Abbreviation:  -     Publication Date:  1999  
Date Detail:
Created Date:  2008-02-06     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101211035     Medline TA:  IEEE Trans Neural Netw     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1153-65     Citation Subset:  -    
Dept. of Electr. and Comput. Eng., Houston Univ., TX.
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