This paper proposes a graph-model-based approach to prioritizing the test process. Tests are ranked according to their preference degrees which are determined indirectly, i.e., through classifying the events. To construct the groups of events, unsupervised neural network is trained by adaptive competitive learning algorithm. A case study demonstrates and validates the approach.
This paper describes the derivation of fuzzy classification rules based on c-means fuzzy clustering algorithm as results that are induced of fuzzy clusters. Each fuzzy cluster is associated with a fuzzy classification rule in which fuzzy sets are obtained by projecting the cluster to one-dimensional domains. In order to provide a unique assignment of data to a defined class it is suggested to use the fuzzy query processing executed on the base of induced linguistic fuzzy classification rules. This approach has been applied to fuzzy classification of population where fast and efficient assignment as well as the rank of a data in the same class is supplied.
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