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Author: Hadjira Bachiri
Publisher:
ISBN:
Category :
Languages : fr
Pages : 0
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Book Description
Dans un contexte de mondialisation et d'économie de la connaissance, des initiatives de clusters ont été largement mises en place partout dans le monde. En effet, la mise en place des clusters a suscité un intérêt particulier à les évaluer et à mesurer leurs performances et leurs impacts. La littérature montre que toutes les problématiques liées à l'évaluation des clusters sont posées de manière très générale, sans vraiment prendre en compte les différentes phases de développement des clusters au cours du temps lors de la définition des critères et des indicateurs d'évaluation. Or, la performance des clusters dans des phases de développement différentes n'est pas du tout la même. Ainsi, les critères et les indicateurs d'évaluation des clusters semblent varier en fonction de leur stade de développement et leur degré de maturité, une analyse de cycle de vie des clusters est nécessaire.Dans ce travail de recherche doctoral nous avons développé un modèle d'évaluation qui regroupe les différentes phases de développement du cluster. Et pour chaque phase de développement nous proposons des critères d'évaluation.Pour ce faire, nous avons choisi une méthodologie de nature qualitative par étude de cas. En effet, nous étudions deux cas de clusters dans le domaine du transport dans deux pays différents : le pôle de compétitivité "Mov'eo" en France et le créneau d'excellence "Transportail" au Québec, avec une comparaison à posteriori.
Author: Hadjira Bachiri
Publisher:
ISBN:
Category :
Languages : fr
Pages : 0
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Book Description
Dans un contexte de mondialisation et d'économie de la connaissance, des initiatives de clusters ont été largement mises en place partout dans le monde. En effet, la mise en place des clusters a suscité un intérêt particulier à les évaluer et à mesurer leurs performances et leurs impacts. La littérature montre que toutes les problématiques liées à l'évaluation des clusters sont posées de manière très générale, sans vraiment prendre en compte les différentes phases de développement des clusters au cours du temps lors de la définition des critères et des indicateurs d'évaluation. Or, la performance des clusters dans des phases de développement différentes n'est pas du tout la même. Ainsi, les critères et les indicateurs d'évaluation des clusters semblent varier en fonction de leur stade de développement et leur degré de maturité, une analyse de cycle de vie des clusters est nécessaire.Dans ce travail de recherche doctoral nous avons développé un modèle d'évaluation qui regroupe les différentes phases de développement du cluster. Et pour chaque phase de développement nous proposons des critères d'évaluation.Pour ce faire, nous avons choisi une méthodologie de nature qualitative par étude de cas. En effet, nous étudions deux cas de clusters dans le domaine du transport dans deux pays différents : le pôle de compétitivité "Mov'eo" en France et le créneau d'excellence "Transportail" au Québec, avec une comparaison à posteriori.
Author: Émilie-Pauline Gallié
Publisher:
ISBN:
Category :
Languages : en
Pages : 35
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Book Description
Although there is a consensus concerning the need for public policy evaluation, there is no stable doctrine regarding the way such assessments should be carried out. Different models coexist or succeed one another; it is, for example, possible to schematically oppose a ballistic model of evaluation “of the action” to an emergent model of evaluation “in the action”. The aim of this article is to analyse the evolution in public policy evaluations and the difficulties inherent in them by studying the French cluster evaluation undertaken in 2008. This evaluation was planned from the beginning as a component of the cluster policy, with the aim of modifying the policy in the light of its initial results.We first put into perspective the doctrines and methodologies underpinning public policy evaluation in general and cluster evaluation in particular. We then study the procedures used in the French cluster evaluation, comparing them to four international cases (Germany, Belgium, Finland and Austria). The analysis is based on a detailed examination of documents relevant to the evaluation, on our empirical knowledge of the French clusters, and on discussions with territorial and national actors involved in the cluster policy.The article reveals the inherent difficulties in cluster evaluation processes. These difficulties are mostly related to the systemic, multi-actor and heterogeneous characteristics of the object “cluster”. Analysing the usage and the effects of the evaluation on the various actors allows us to conclude that cluster evaluation in France is a learning source for the progressive construction of a cluster doctrine and a doctrine of its management. The evaluation, grounded in an interactive approach, becomes part of a larger process, a knowledge process benefiting both the government and the local actors concerned. Integrated from the outset into the cluster management system, the evaluation becomes a tool amongst others; it is therefore less consistent with a model of objective, incontestable and independent knowledge production than with an instrument to help decision-makers forge their choices.
Author: Bjorn Asheim
Publisher: Routledge
ISBN: 1134273592
Category : Business & Economics
Languages : en
Pages : 371
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Book Description
Using international examples, leading scholars present the first critical analysis of cluster theory, assessing the cluster notion and drawing out, not only its undoubted strengths and attractions, but also its weaknesses and limitations. Over the past decade the ‘cluster model’ has been seized on as a tool for promoting competitiveness, innovation and growth on local, regional and national scales. However, despite its popularity there is much about it that is problematic, and in some respects the rush to employ ‘cluster ideas’ has run ahead of many fundamental conceptual, theoretical and empirical questions. Addressing key questions on the nature, use and effectiveness of cluster models, Clusters and Regional Development provides the missing thorough theoretical and empirical evaluation.
Author: E.J. Bynen
Publisher: Springer Science & Business Media
ISBN: 9401167826
Category : Social Science
Languages : en
Pages : 122
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Book Description
During the last years the number of applications of cluster analysis in the social sciences has increased very rapidly. One of the reasons for this is the growing awareness that the assumption of homogeneity implicit in the application of such techniques as factor analysis and scaling is often violated by social science data; another is the increased interest in typolo gies and the construction of types. Dr. Bijnen has done an extremely useful job by putting together and evaluating attempts to arrive at better and more elegant techniques of cluster analysis from such diverse fields as the social sciences, biology and medicine. His presentation is very clear and concise, reflecting his intention not to write a 'cookery-book' but a text for scholars who need a reliable guide to pilot them through an extensive and widely scattered literature. Ph. C. Stouthard v Preface This book contains a survey of a number of techniques of clustering analysis. The merits and demerits of the procedures described are also discussed so that the research worker can make an informed choice be tween them. These techniques have been published in a very great number of journals which are not all easily accessible to the sociologist. This difficulty is com pounded because developments in the different disciplines have occurred almost entirely independently from each other; reference is made only sporadically in a piece of literature to the literature of other disciplines.
Author: Ibrahim Aljarah
Publisher: Springer Nature
ISBN: 9813341912
Category : Technology & Engineering
Languages : en
Pages : 248
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Book Description
This book provides an in-depth analysis of the current evolutionary clustering techniques. It discusses the most highly regarded methods for data clustering. The book provides literature reviews about single objective and multi-objective evolutionary clustering algorithms. In addition, the book provides a comprehensive review of the fitness functions and evaluation measures that are used in most of evolutionary clustering algorithms. Furthermore, it provides a conceptual analysis including definition, validation and quality measures, applications, and implementations for data clustering using classical and modern nature-inspired techniques. It features a range of proven and recent nature-inspired algorithms used to data clustering, including particle swarm optimization, ant colony optimization, grey wolf optimizer, salp swarm algorithm, multi-verse optimizer, Harris hawks optimization, beta-hill climbing optimization. The book also covers applications of evolutionary data clustering in diverse fields such as image segmentation, medical applications, and pavement infrastructure asset management.
Author: Charu C. Aggarwal
Publisher: CRC Press
ISBN: 1466558229
Category : Business & Economics
Languages : en
Pages : 648
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Book Description
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.
Author: Gérard Govaert
Publisher: John Wiley & Sons
ISBN: 1118649508
Category : Computers
Languages : en
Pages : 246
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Book Description
Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixtures adapted to different types of data. The algorithms used are described and related works with different classical methods are presented and commented upon. This chapter is useful in tackling the problem of co-clustering under the mixture approach. Chapter 2 is devoted to the latent block model proposed in the mixture approach context. The authors discuss this model in detail and present its interest regarding co-clustering. Various algorithms are presented in a general context. Chapter 3 focuses on binary and categorical data. It presents, in detail, the appropriated latent block mixture models. Variants of these models and algorithms are presented and illustrated using examples. Chapter 4 focuses on contingency data. Mutual information, phi-squared and model-based co-clustering are studied. Models, algorithms and connections among different approaches are described and illustrated. Chapter 5 presents the case of continuous data. In the same way, the different approaches used in the previous chapters are extended to this situation. Contents 1. Cluster Analysis. 2. Model-Based Co-Clustering. 3. Co-Clustering of Binary and Categorical Data. 4. Co-Clustering of Contingency Tables. 5. Co-Clustering of Continuous Data. About the Authors Gérard Govaert is Professor at the University of Technology of Compiègne, France. He is also a member of the CNRS Laboratory Heudiasyc (Heuristic and diagnostic of complex systems). His research interests include latent structure modeling, model selection, model-based cluster analysis, block clustering and statistical pattern recognition. He is one of the authors of the MIXMOD (MIXtureMODelling) software. Mohamed Nadif is Professor at the University of Paris-Descartes, France, where he is a member of LIPADE (Paris Descartes computer science laboratory) in the Mathematics and Computer Science department. His research interests include machine learning, data mining, model-based cluster analysis, co-clustering, factorization and data analysis. Cluster Analysis is an important tool in a variety of scientific areas. Chapter 1 briefly presents a state of the art of already well-established as well more recent methods. The hierarchical, partitioning and fuzzy approaches will be discussed amongst others. The authors review the difficulty of these classical methods in tackling the high dimensionality, sparsity and scalability. Chapter 2 discusses the interests of coclustering, presenting different approaches and defining a co-cluster. The authors focus on co-clustering as a simultaneous clustering and discuss the cases of binary, continuous and co-occurrence data. The criteria and algorithms are described and illustrated on simulated and real data. Chapter 3 considers co-clustering as a model-based co-clustering. A latent block model is defined for different kinds of data. The estimation of parameters and co-clustering is tackled under two approaches: maximum likelihood and classification maximum likelihood. Hard and soft algorithms are described and applied on simulated and real data. Chapter 4 considers co-clustering as a matrix approximation. The trifactorization approach is considered and algorithms based on update rules are described. Links with numerical and probabilistic approaches are established. A combination of algorithms are proposed and evaluated on simulated and real data. Chapter 5 considers a co-clustering or bi-clustering as the search for coherent co-clusters in biological terms or the extraction of co-clusters under conditions. Classical algorithms will be described and evaluated on simulated and real data. Different indices to evaluate the quality of coclusters are noted and used in numerical experiments.
Author: Saurabh Kumar Singhal
Publisher:
ISBN:
Category :
Languages : en
Pages : 260
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Author: E J Bynen
Publisher:
ISBN: 9789401167833
Category :
Languages : en
Pages : 128
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Author:
Publisher:
ISBN:
Category : Psychiatry
Languages : en
Pages : 992
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