Advances in Fuzzy Systems
Volume 2015 (2015), Article ID 957856, 1 page
http://dx.doi.org/10.1155/2015/957856

Editorial

Fuzzy Methods for Data Analysis

Ferdinando Di Martino,¹ Irina Perfilieva,² and Salvatore Sessa1,3

¹Dipartimento di Costruzioni e Metodi Matematici in Architettura, Università degli Studi di Napoli Federico II, Via Monteoliveto 3, 80134 Napoli, Italy
²Centre of Excellence IT4Innovations, Division of the University of Ostrava, Institute for Research and Applications of Fuzzy Modeling, 30. Dubna 22, 701 00 Ostrava 1, Czech Republic
³Centro Interdipartimentale di Ricerca per l’Analisi e la Progettazione Urbana Luigi Pisciotti, Via Toledo 402, 80134 Napoli, Italy

Received 9 December 2014; Accepted 9 December 2014

Copyright © 2015 Ferdinando Di Martino et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This special issue contains various papers where the topic of data analysis is studied with various fuzzy tools. The following is a short summary of what has been considered.

The paper of R. Li and G. Li deals with 20 samples and 44 samples of terracotta warriors and horses of Qin Shi Huang’s Mausoleum 20 samples of clay near Qin’s Mausoleum, and 2 samples of Yaozhou porcelain bodies, and in each of them the contents of 32 elements are determined by using the Neutron Activation Analysis whose data are analyzed with a fuzzy cluster algorithm: the conclusion is that the terracotta of the above artefacts originates from different near sites.

The paper of J. Davis et al. deals with fuzzy probability of screening a survey data across relevant criteria for selecting suppliers based on fuzzy expected values that are influenced by a delivery performance.

The paper of A. Chaudhuri deals with an intuitionistic fuzzy possibilistic C-means algorithm which determines membership values of objects to each cluster as intervals instead of single numerical values, so classifying datasets with labelled patterns.

The paper of M. Burda provides a correct definition of lift, leverage, and conviction measures for fuzzy association rules, studying the related properties.

The paper of P. Shanmugasundaram et al. deals with a revised intuitionistic fuzzy max-min average composition method in order to select well-trained students on the basis of their skills by the recruiters by means of intuitionistic fuzzy soft matrices.

We hope that this issue evokes deep interest among fuzzy authors and not alone.

Ferdinando Di Martino
Irina G. Perfilieva
Salvatore Sessa