Title :
A c-fuzzy means algorithm for prototype induction
Author :
Baldwin, J.F. ; Lawry, J.
Author_Institution :
Dept. of Eng. Math., Bristol Univ., UK
Abstract :
A c-fuzzy means algorithm is described. The algorithm learns fuzzy prototypes to represent data sets and is based on ideas taken from mass assignment theory. The potential of this approach for both unsupervised and supervised learning is illustrated by its application to a number of benchmark and model problems
Keywords :
fuzzy set theory; inference mechanisms; knowledge representation; learning (artificial intelligence); c-fuzzy means algorithm; fuzzy set theory; knowledge representation; mass assignment theory; prototype induction; supervised learning; unsupervised learning; Data mining; Databases; Design engineering; Fuzzy set theory; Fuzzy sets; Machine learning; Mathematics; Predictive models; Probability distribution; Prototypes;
Conference_Titel :
Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-5877-5
DOI :
10.1109/FUZZY.2000.838652
