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个人简介
He developed the fuzzy c-means (FCM) algorithm, considered one of the most important discoveries in fuzzy pattern recognition and related areas and the clustering algorithm of choice for most practitioners in fuzzy exploratory data analysis. The original model has inspired many applications in related areas of pattern recognition and image processing.
Areas of research benefiting from Dr. Bezdek’s work include diagnostic medicine, economics, chemistry, image processing, meteorology, web mining, geology, target recognition, regression analysis, document retrieval, structural failure and irrigation models. One of the most notable applications has been in medical image analysis, where FCM segmentation of magnetic resonance images is used in conjunction with rule-based analysis for both diagnosis and pre-operative planning for brain tumor patients. Dr. Bezdek also has made pioneering contributions in deriving the theories for clustering of relational (Euclidean and non-Euclidean) data.
Areas of research benefiting from Dr. Bezdek’s work include diagnostic medicine, economics, chemistry, image processing, meteorology, web mining, geology, target recognition, regression analysis, document retrieval, structural failure and irrigation models. One of the most notable applications has been in medical image analysis, where FCM segmentation of magnetic resonance images is used in conjunction with rule-based analysis for both diagnosis and pre-operative planning for brain tumor patients. Dr. Bezdek also has made pioneering contributions in deriving the theories for clustering of relational (Euclidean and non-Euclidean) data.
研究兴趣
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2019 IEEE Symposium Series on Computational Intelligence (SSCI) (2019)
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