Comparison of different strategies of utilizing fuzzy clustering in structure identification

Fuzzy systems approximate highly nonlinear systems by means of fuzzy "if-then" rules. In the literature, various algorithms are proposed for mining. These algorithms commonly utilize fuzzy clustering in structure identification. Basically, there are three different approaches in which one can utilize fuzzy clustering; the �first one is based on input space clustering, the second one considers clustering realized in the output space, while the third one is concerned with clustering realized in the combined input-output space. In this study, we analyze these three approaches. We discuss each of the algorithms in great detail and o¤er a thorough comparative analysis. Finally, we compare the performances of these algorithms in a medical diagnosis classi�cation problem, namely Aachen Aphasia Test. The experiment and the results provide a valuable insight about the merits and the shortcomings of these three clustering approaches

Multi-Dimensional Analysis of Poverty in Ghana Using Fuzzy Sets Theory

The paper studies the multidimensional aspects of poverty and living conditions in Ghana. The aim is to fill the vacuum that has been left by traditional uni-dimensional measures of deprivation based on poverty lines, exclusively estimated on the basis of monetary variables such as income or consumption expenditure. It combines monetary and non-monetary, and qualitative and quantitative indicators, including housing conditions, the possession of durable goods, equivalent disposable income, and equivalent expenditure, with a number of composite human welfare measures. The study employs the fuzzy-set theoretic framework to compare levels of deprivation in Ghana over time usig micro data from the last two rounds of the Ghana Living Standard Surveys (1991/1992 and 1998/1999). The estimation results of the membership functions, depicting the levels of deprivation for the various categories of deprivation indicators, show a composite deprivation degree of 0.2137 for the whole country in 1998/99 as compared to 0.2123 in 1991/92. This deprivation trend reveals that poverty levels hard scarcely changed in Ghana. In fact, it even rose slightly during the nineties, contrary to the uni-dimensional analytical GLSS 4 report of an overall broadly favourable trend in poverty in Ghana during the 1990s.Ghana, fuzzy set, multi-dimensional poverty, composite deprivation or poverty index

Multidimensional Poverty Comparisons within Europe. Evidence from the European Community Household Panel

This paper is a cross-sectional study on multidimensional poverty comparisons among the European Union countries, based on data provided by the European Community Household Panel (ECHP). In addition to the empirical results and the methodological problems, the study underlines the opportunities and the difficulties met while using the ECHP. The extended concept of poverty is relative and multidimensional and it reflects not only the financial aspects, but also dimensions like family composition, leisure, subjective deprivation, social participation, durable goods, housing conditions, access to education. Hence, it requires comparative assessments through ordinal measures. In order to compare the multidimensional poverty in 1999 and in a time interval (1994-1999), we have applied the Totally Fuzzy and Relative Method (TFR) in two forms: original (Cheli and Lemmi, 1995) and alternative (Cheli, D’Agostino and Filippone, 2001). The research reveals the hierarchy of countries according to different indicators of poverty. Although the rankings given by the two methods are similar in some parts, there are differences establishing the issues which arise when different features of deprivation are aggregated into a collective index. We show that the variables taken into account, the method and its interpretability, the data and the national particularities, they all have a big influence on the relative and comparative measurement of poverty.multidimensional poverty ; fuzzy set theory ; poverty comparisons ; poverty measurement ; well-being assessment

Graph ambiguity

In this paper, we propose a rigorous way to define the concept of ambiguity in the domain of graphs. In past studies, the classical definition of ambiguity has been derived starting from fuzzy set and fuzzy information theories. Our aim is to show that also in the domain of the graphs it is possible to derive a formulation able to capture the same semantic and mathematical concept. To strengthen the theoretical results, we discuss the application of the graph ambiguity concept to the graph classification setting, conceiving a new kind of inexact graph matching procedure. The results prove that the graph ambiguity concept is a characterizing and discriminative property of graphs. (C) 2013 Elsevier B.V. All rights reserved

Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses

A Modular Programmable CMOS Analog Fuzzy Controller Chip

We present a highly modular fuzzy inference analog CMOS chip architecture with on-chip digital programmability. This chip consists of the interconnection of parameterized instances of two different kind of blocks, namely label blocks and rule blocks. The architecture realizes a lattice partition of the universe of discourse, which at the hardware level means that the fuzzy labels associated to every input (realized by the label blocks) are shared among the rule blocks. This reduces the area and power consumption and is the key point for chip modularity. The proposed architecture is demonstrated through a 16-rule two input CMOS 1-μm prototype which features an operation speed of 2.5 Mflips (2.5×10^6 fuzzy inferences per second) with 8.6 mW power consumption. Core area occupation of this prototype is of only 1.6 mm 2 including the digital control and memory circuitry used for programmability. Because of the architecture modularity the number of inputs and rules can be increased with any hardly design effort.This work was supported in part by the Spanish C.I.C.Y.T under Contract TIC96-1392-C02- 02 (SIVA)