Using fuzzy clustering to improve naive Bayes classifiers and probabilistic networks
Christian Borgelt, Heiko Timm, Rudolf Kruse · 2002
Although probabilistic networks and fuzzy clustering may seem to be disparate areas of research, they can both be seen as generalizations of naive Bayes classifiers. If all descriptive attributes are numeric, naive Bayes classifiers often assume an axis-parallel multidimensional normal distribution for each class. Probabilistic networks remove the requirement that the distributions must be axis-parallel by taking covariances into account where this is necessary. Fuzzy clustering tries to find general or axis-parallel distributions to cluster the data. Although it neglects the classification information, it can be used to improve the result of the above mentioned methods by removing the restriction to only one distribution per classification.