In this paper, we use fuzzy clustering to investigate three datasets of software metrics, along with the larger issue of whether supervised or unsupervised learning is more appropriate for software engineering problems.
This list was submitted to the Database for Annotation, Visualization, and Integrated Discovery (DAVID), which uses fuzzy clustering to group genes into functionally related, statistically ranked classes, based on the similarity of the annotations (http://david.abcc.ncifcrf.gov) [ 24].
Functional annotations of gene loci were compared with the complete genome using annotations from the Database for Annotation, Visualization, and Discovery (DAVID), which uses fuzzy clustering to group genes into functionally related classes based on the similarity of their annotations [ 84, 85].
Functional annotations of these loci were compared with the complete genome using annotations from the Database for Annotation, Visualization, and Discovery (DAVID) (Huang et al. 2007, 2009), which uses fuzzy clustering to group genes into functionally related classes based on the similarity of their annotations.
Enhancing contrast by using fuzzy clustering allows to define the contours of the lesion correctly.
When fuzzy systems are designed by using fuzzy clustering, each cluster corresponds to a fuzzy rule.
Fault pattern space is first divided to subspaces using fuzzy clustering algorithm.
The reason for using fuzzy clustering and not crisp clustering is to reveal uncertainties in the cluster assignments [24].
In order to develop fuzzy models, using fuzzy clustering techniques for parameter identification, the model structure given by equation (6) was used.
In this paper an approach to design data driven based fault diagnosis systems using fuzzy clustering techniques is presented.
