Hiroaki Uesu

Sociometry analysis applying fuzzy node fuzzy graph

Sociometry is a social structure measurement and evaluation method, which we can effectively analyze by applying fuzzy graph theory. We extend the fuzzy graph theory, and propose a fuzzy node fuzzy graph, and we transform it to a crisp node fuzzy graph using by T-norm. The authors explain a fuzzy node fuzzy graph, and propose new T-norm family quasi logical product. By using this new T-norm, we could reasonably transform the fuzzy node fuzzy graph to the crisp node fuzzy graph.

Learning structure analysis system applying fuzzy theory

The inexact phenomena such as the mental process and cognition would effectively be analyzed by using the fuzzy graph. In this paper, we would explain about the learning structure analysis system by applying fuzzy graph, present its practical case study and the effectiveness.

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.

Fuzzy Cluster Analysis and Its Application on International Stock Prices

This paper applies fuzzy cluster analysis to investigate co movement of Asian and U.S. stock prices from the viewpoints of both region and industry. Specifically, we analyze daily stock price data of Chinese, Indian, Japanese, South Korean, and U.S. firms from 2005 through 2011. The past literature has never used daily data because of non-synchronous trading times and holidays, but we resolve this problem by analyzing American depositary receipts traded in the New York Stock Exchange instead of underlying shares traded all over the world. Partition trees computed each year provide overwhelming evidence that the country effect always surpasses the industry effect (i.e., shares from the same country tend to move together but shares within the same industry do not). This finding is particularly informative for portfolio managers, choosing a country and then many kinds of industry therein is a riskier strategy than choosing an industry and then many countries. Besides this practical implication, the dominant country effect highlights a slow process of globalization. Nationality of shares should not matter in a globalized world, but there still exist barriers segmenting countries. All these results and implications are robust to different clustering methods, the frequency of data, and foreign exchange rates.

Mathematical analysis of similarity index and connectivity index in fuzzy graph

We often represent the inexact phenomena regarding mental process and cognition as fuzzy graphs. If we investigate the cluster and the order of the nodes in the fuzzy graph, we have a lot of interesting results. For this purpose we define the similarity Index and the connectivity Index. In this paper, we would discuss the definition of the indices and its properties.

Fuzzy graph analysis for sociometry on latticed display

The fuzzy graph will make it possible to quantitatively analyze fuzzy information such as expression of human relations and mental processes. To clarify the main feature of a fuzzy graph, we would represent it as an approximate graph and extract its characteristics such as similarity and connectivity structures. Therefore we must proceed to analyze many kinds of information concerning the structure of a fuzzy graph, such as drawing and displaying comprehensively fuzzy graphs in the process. We have developed a computer-aided method for analyzing fuzzy graphs through human interaction. This method can quickly and comprehensively draw a graph arranged on a circular, a partition tree, cluster representative graph corresponding to a cluster and a specified shape of approximate n-valued fuzzy graph. However, it can not draw automatically a graph with nodes on the lattice intersection. So, we would rearrange the nodes on the lattice intersection for any fuzzy graph through user interface. This display is convenient for traditionally analyzing fuzzy graphs. We propose an analysis method using the lattice type of fuzzy graph display. Here, it plays an important role in this system. In this paper, we describe the analysis method and user interface of this system, and their application to sociometry analysis.