this paper presents a fuzzy decision-making approach to deal with a clustering supplier problem in a supply chain system. during recent years, determining suitable suppliers in the supply chain has become a key strategic consideration. however, the nature of these decisions is usually complex and unstructured. in general, many quantitative and qualitative factors, such as quality, price, and fl...

Tourism is one of the world’s largest and fastest growing industries. Industrial cluster theory is perhaps the leading model for economic development today. Despite this, the academic literature of industrial cluster theory has paid relatively little attention to the tourism industry. Meanwhile, practitioners of tourism development are experimenting with the cluster concept across the US and ar...

This paper presents a methodology for knowledge acquisition from source code. We use data mining to support semiautomated software maintenance and comprehension and provide practical insights into systems specifics, assuming one has limited prior familiarity with these systems. We propose a methodology and an associated model for extracting information from object oriented code by applying clus...

Man-machine systems have many features that are to be considered simultaneously. Their validation often leads to a large number of tests; due to time and cost constraints they cannot exhaustively be run. It is then essential to prioritize the test subsets in accordance with their importance for relevant features. This paper applies soft-computing techniques to the prioritizing problem and propo...

A pairwise clustering approach is applied to the analysis of the Dow Jones index companies, in order to identify similar temporal behavior of the traded stock prices. To this end, the chaotic map clustering algorithm is used, where a map is associated to each company and the correlation coefficients of the financial time series are associated to the coupling strengths between maps. The simulati...

This paper addresses the same quality management problem as Ou and Wein (1992), except that here screening is performed at the chip level, rather than at the wafer level. We analyze over 300 wafers from two industrial facilities and use a Markov random field model to capture the spatial clustering of bad chips. Chip screening strategies are proposed that exploit the various types of yield nonun...

This work at the Laboratory for Industrial and Applied Mathematics on the theoretical foundation and applications of projected clustering of high dimensional and big data has been supported by a number of programs and funding agencies including the Canada Research Chairs program, the Natural Sciences and Engineering Research Council of Canada (discovery grant, collaborative research development...

This paper describes a novel fuzzy rule-based modeling approach for some slow industrial processses. Structure identification is realized by clustering and support vector machines. When the process is slow, fuzzy rules can be obtained automatically. Parameters identification uses the techniques of fuzzy neural networks. A time-varying learning rate assures stability of the modeling error.

A clustering procedure is introduced based on the Hausdorff distance as a similarity measure between clusters of elements. The method is applied to the financial time series of the Dow Jones industrial average (DJIA) index to find companies that share a similar behavior. Comparisons are made with other linkage algorithms. r 2007 Elsevier B.V. All rights reserved.

We compare three network portfolio selection methods; hierarchical clustering trees, minimum spanning trees and neighbor-Nets, with random and industry group selection methods on twelve years of data from the 30 Dow Jones Industrial Average stocks from 2001 to 2013 for very small private investor sized portfolios. We find that the three network methods perform on par with randomly selected port...