Ei Tsuda

Fuzzy Clustering Level Analysis Using AIC Method for Large Size Samples

This paper investigates the fuzzy clustering level analysis using AIC (Akaikes information criterion) method for small size samples. Since AIC is obtained by the asymptotic normality for the maximal likelihood estimator, it is difficult to apply it to small size samples. Therefore, in the paper, we would show that the AIC method can be applied to large size samples which are constructed by a simulation with pseudo random numbers obeying several distributions.

Instruction and cognition analysis applying fuzzy graphs

Abstract The inexact phenomena concerning human behavior, mental processes, and cognition are reasonably represented as fuzzy graphs. In order to clarify the global features of such fuzzy information, we analyze them from the points of view similarity and connectivity. We recently developed an analytical method concerning the cognitive structure of a subject by applying fuzzy theory, which enables us to verify and / or modify instruction programs on the basis of test data. In order to carry out the data analysis accurately and quickly, a computer support system was developed and has been effectively utilized. In this paper, we discuss the theoretical explanations of the analysis system and illustrate its instruction effectiveness through case studies in mathematics.