Classical radial basis function network (RBFN) is widely used to process the non-linear separable data sets with the introduction of activation functions. However, the setting of parameters for activation functions is random and the distribution of patterns is not taken into account. To process this issue, some scholars introduce the kernel clustering into the RBFN so that the clustering results are related to the parameters about activation functions. On the base of the original kernel clustering, this study further discusses the influence of kernel clustering on an RBFN when the setting of kernel clustering is changing. The changing involves different kernel-clustering ways [bubble sort (BS) and escape nearest outlier (ENO)], multiple kernel-clustering criteria (static and dynamic) etc. Experimental results validate that with the consideration of distribution of patterns and the changes of setting of kernel clustering, the performance of an RBFN is improved and is more feasible for corresponding data sets. Moreover, though BS always costs more time than ENO, it still brings more feasible clustering results. Furthermore, dynamic criterion always cost much more time than static one, but kernel number derived from dynamic criterion is fewer than the one from static.

Fuzzy decision implication is an extension of decision implication in the fuzzy setting, serving to uncover the dependencies of fuzzy attributes. This study presents the interpretation of fuzzy decision implication in the fuzzy decision context. Specially, they will show that from fuzzy decision contexts one can obtain a closed fuzzy set of fuzzy decision implications, and the semantical characteristic of the obtained fuzzy set can be interpreted by fuzzy decision context and can be represented by some operators of fuzzy decision context. Conversely, starting from a fuzzy set of fuzzy decision implications, they can form a fuzzy decision context, from which the given fuzzy set can be derived. The result actually implies that they have constructed a correspondence between closed fuzzy sets of fuzzy decision implications and fuzzy decision contexts, and thus shows the equivalence of two interpretations of fuzzy decision implications.