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Journal ArticleDOI

Rough Clustering of Destination Image Data Using an Evolutionary Algorithm

15 Aug 2007-Journal of Travel & Tourism Marketing (Taylor & Francis Group)-Vol. 21, Iss: 4, pp 121-137
TL;DR: The article describes the template, the data structure used to describe rough clusters, and provides an overview of the evolutionary algorithm used to develop viable cluster solutions, consisting of an optimal number of templates, which provide easily interpreted descriptions of the clusters.
Abstract: This article describes a hybrid computational intelligence technique, an evolutionary algorithm based rough clustering algorithm. The technique of cluster analysis is fundamental in traditional data analysis and data mining, and is used to group similar objects together. Many clustering methods have been identified, including the commonly used k-means approach, which is dependent on initial starting points and requires the number of clusters to be specified in advance. The rough clustering algorithm described in this article is able to overcome these limitations. Rough clusters are defined in a similar manner to the rough set concept developed by Pawlak—that is, using a lower and upper approximation. This allows for multiple cluster membership for objects in the data set. The lower approximation of a rough cluster contains objects that only belong to that cluster. The upper approximation of a rough cluster contains objects that belong to more than one cluster. The article describes the template, ...
Citations
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Journal ArticleDOI
TL;DR: A comprehensive review of conceptual and empirical research on destination image published between January 2000 and October 2007 is provided with the aim of identifying current and emerging trends in the area of destination image studies.
Abstract: Determining destination image is a complex task, and the construct is often subjectively assessed. No standardized system of analysis exists to determine destination image and its related components, resulting in a variety of techniques and strategies being used. This study provides a comprehensive review of conceptual and empirical research on destination image published between January 2000 and October 2007 with the aim of identifying current and emerging trends in the area of destination image studies. Meta-analysis of 152 articles that discuss various strategies for destination image assessment summarizes the state of destination image research and examines new destination image assessment approaches. The findings are contrasted to earlier destination image reviews where appropriate.

254 citations


Cites methods from "Rough Clustering of Destination Ima..."

  • ...Voges (2006) employed a hybrid computational intelligence technique developed for estimating mechanics of natural evolution for clustering objects like perceivers of images; the researcher argued that this approach is better suites than k-clustering techniques traditionally used in segmentation…...

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Journal ArticleDOI
TL;DR: In this article, a new approach that extends the capability of the association rules technique to contrast targeted association rules with the aim of capturing the changes and trends in outbound tourism is presented.

51 citations

Journal ArticleDOI
TL;DR: In this paper, a study aims to cluster the world's top tourist destinations based on the growth of the main tourism indicators over the period between 2000 and 2010, and ranks the destinations with respect to the average growth rate over the sample period.
Abstract: This study aims to cluster the world’s top tourist destinations based on the growth of the main tourism indicators over the period between 2000 and 2010. It ranks the destinations with respect to the average growth rate over the sample period. The results find that both China and Turkey are at the top of the rankings of all variables. By assigning a numerical value to each country corresponding to its position, a Spearman’s coefficient is calculated and a negative correlation found between a destination’s dependency on tourism and the profitability of the tourism activity. Finally, several multivariate techniques for dimensionality reduction are used to cluster all destinations according to their positioning. Three groups are obtained: China, Turkey, and the rest of the destinations. These results show that the persistent growth of the tourism industry poses different challenges in different markets regarding destination marketing and management.

34 citations


Additional excerpts

  • ...…is widely used is market segmentation studies (Dey & Sarma, 2010; Donaire, Camprubí & Galí, 2014; Keng & Cheng, 1999; Lee, Lee, Bernhard & Yoon, 2006; Park & Yoon, 2009; Rid, Ezeuduji & Pröbstl-Haider, 2014; Sinclari-Maragh, Gursoy & Vieregge, 2015; Upchurch, Ellis & Seo, 2004; Voges, 2007)....

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Journal ArticleDOI
TL;DR: Visualizing the destination countries’ similarity in terms of Web-projected connotative profiles highlights one aspect of competitive threat.
Abstract: Emotion-carrying connotations are regarded as constituents of destination images. The connotative word items symptomatically associated with country names are likely to influence the emotional state and hence arousal level of Web users when browsing through tourism-related pages. The co-occurrence frequencies of connotative nouns and tourism-receiving country names in Web content serve as a basis for portraying the countries’ positions in connotative space. Transforming the raw frequencies into Normalized Google Distances makes them comparable. Automated data retrieval operates for the Web domain defined by “tourism OR tourist OR travel OR trip.” Visualizing the destination countries’ similarity in terms of Web-projected connotative profiles highlights one aspect of competitive threat. It is demonstrated with disjunctive and nondisjunctive hierarchical clustering, network analysis, and Sammon mapping.

19 citations


Cites background from "Rough Clustering of Destination Ima..."

  • ...Voges (2006) applies this approach for city image data of urban destinations in the Asia Pacific region....

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Journal ArticleDOI
TL;DR: In this paper, the authors focus on the interactions between tourism and economic variables in twenty emerging markets and provide a descriptive analysis and rank the countries according to their percentage average annual growth in relation to a set of economic and tourism indicators during the last decade.

18 citations


Cites background or methods from "Rough Clustering of Destination Ima..."

  • ...…DR techniques are widely implemented is in market segmentation studies (Guo et al., 2015; SinclariMaragh et al., 2015; Donaire et al, 2014; Rid et al., 2014; Dey & Sarma, 2010; Park & Yoon, 2009; Voges, 2007; Lee et al., 2006; Upchurch et al., 2004; Arimond & Elfessi; 2001; Keng & Cheng, 1999)....

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  • ...Notwithstanding, one of the main areas in which DR techniques are widely implemented is in market segmentation studies (Guo et al., 2015; SinclariMaragh et al., 2015; Donaire et al, 2014; Rid et al., 2014; Dey & Sarma, 2010; Park & Yoon, 2009; Voges, 2007; Lee et al., 2006; Upchurch et al., 2004; Arimond & Elfessi; 2001; Keng & Cheng, 1999)....

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References
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Book
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required

52,797 citations

Journal ArticleDOI
01 Jan 1973
TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each. Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations. Table of Contents 1. Introduction. I. PREPARING FOR A MULTIVARIATE ANALYSIS. 2. Examining Your Data. 3. Factor Analysis. II. DEPENDENCE TECHNIQUES. 4. Multiple Regression. 5. Multiple Discriminant Analysis and Logistic Regression. 6. Multivariate Analysis of Variance. 7. Conjoint Analysis. 8. Canonical Correlation Analysis. III. INTERDEPENDENCE TECHNIQUES. 9. Cluster Analysis. 10. Multidimensional Scaling. IV. ADVANCED AND EMERGING TECHNIQUES. 11. Structural Equation Modeling. 12. Emerging Techniques in Multivariate Analysis. Appendix A: Applications of Multivariate Data Analysis. Index.

37,124 citations

Book
01 Jan 1975
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.
Abstract: Name of founding work in the area. Adaptation is key to survival and evolution. Evolution implicitly optimizes organisims. AI wants to mimic biological optimization { Survival of the ttest { Exploration and exploitation { Niche nding { Robust across changing environments (Mammals v. Dinos) { Self-regulation,-repair and-reproduction 2 Artiicial Inteligence Some deenitions { "Making computers do what they do in the movies" { "Making computers do what humans (currently) do best" { "Giving computers common sense; letting them make simple deci-sions" (do as I want, not what I say) { "Anything too new to be pidgeonholed" Adaptation and modiication is root of intelligence Some (Non-GA) branches of AI: { Expert Systems (Rule based deduction)

32,573 citations


"Rough Clustering of Destination Ima..." refers background or methods in this paper

  • ...A numberofdifferentapproaches toevolutionary algorithms were independently developed at around the same time, including genetic algorithms (Holland, 1975), evolution strategies (Rechenberg, 1973; Schwefel, 1977), and evolutionary programming (L....

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  • ...Theoretical justifications have been advanced that binary representation produces the best solution (Holland, 1975), although this has been vigorously disputed....

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  • ...A numberofdifferentapproaches toevolutionary algorithms were independently developed at around the same time, including genetic algorithms (Holland, 1975), evolution strategies (Rechenberg, 1973; Schwefel, 1977), and evolutionary programming (L. Fogel, Owens & Walsh, 1966)....

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  • ...In the United States, Holland (1975) proposed the general scheme for the genetic algorithm, in his seminal book discussing the process in adaptation in both natural and artificial systems....

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  • ...Examples include structure, gene, chromosome, solution, rule, etc. Holland (1975) referred to the models as structures....

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01 Jan 1967
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Abstract: The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably efficient in the sense of within-class variance. That is, if p is the probability mass function for the population, S = {S1, S2, * *, Sk} is a partition of EN, and ui, i = 1, 2, * , k, is the conditional mean of p over the set Si, then W2(S) = ff=ISi f z u42 dp(z) tends to be low for the partitions S generated by the method. We say 'tends to be low,' primarily because of intuitive considerations, corroborated to some extent by mathematical analysis and practical computational experience. Also, the k-means procedure is easily programmed and is computationally economical, so that it is feasible to process very large samples on a digital computer. Possible applications include methods for similarity grouping, nonlinear prediction, approximating multivariate distributions, and nonparametric tests for independence among several variables. In addition to suggesting practical classification methods, the study of k-means has proved to be theoretically interesting. The k-means concept represents a generalization of the ordinary sample mean, and one is naturally led to study the pertinent asymptotic behavior, the object being to establish some sort of law of large numbers for the k-means. This problem is sufficiently interesting, in fact, for us to devote a good portion of this paper to it. The k-means are defined in section 2.1, and the main results which have been obtained on the asymptotic behavior are given there. The rest of section 2 is devoted to the proofs of these results. Section 3 describes several specific possible applications, and reports some preliminary results from computer experiments conducted to explore the possibilities inherent in the k-means idea. The extension to general metric spaces is indicated briefly in section 4. The original point of departure for the work described here was a series of problems in optimal classification (MacQueen [9]) which represented special

24,320 citations


"Rough Clustering of Destination Ima..." refers methods in this paper

  • ...One of the most commonly used nonhierarchical methods is the k-means approach (MacQueen, 1967)....

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Journal ArticleDOI
TL;DR: This chapter discusses Structural Equation Modeling: An Introduction, and SEM: Confirmatory Factor Analysis, and Testing A Structural Model, which shows how the model can be modified for different data types.
Abstract: I Introduction 1 Introduction II Preparing For a MV Analysis 2 Examining Your Data 3 Factor Analysis III Dependence Techniques 4 Multiple Regression Analysis 5 Multiple Discriminate Analysis and Logistic Regression 6 Multivariate Analysis of Variance 7 Conjoint Analysis IV Interdependence Techniques 8 Cluster Analysis 9 Multidimensional Scaling and Correspondence Analysis V Moving Beyond the Basic Techniques 10 Structural Equation Modeling: Overview 10a Appendix -- SEM 11 CFA: Confirmatory Factor Analysis 11a Appendix -- CFA 12 SEM: Testing A Structural Model 12a Appendix -- SEM APPENDIX A Basic Stats

23,353 citations