cluster analysis

Cluster analysis

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Clustering (199)
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Here, typical techniques of cluster analysis or multivariate statistics can be used. The modelsynset created in phase 3 can support the grouping.

Corresponding instruments should be developed by program owners to provide needbased support for cluster managements. 80 THE AUTHORS THOMAS LÄMMER-GAMP is Director of the European Secretariat for Cluster analysis (ESCA) at VDI/VDE

72 4. 4 Cluster analysis...73 4. 5 Other methods for multivariate analysis...79 6 HANDBOOK ON CONSTRUCTING COMPOSITE INDICATORS:

-Young-Levenglick CLA Cluster analysis DEA Data Envelopment Analysis DFA Discriminant Function Analysis DQAF Data Quality Framework EC European commission EM Expected

, principal components analysis, cluster analysis). ) To identify groups of indicators or groups of countries that are statistically similar

Cluster analysis is another tool for classifying large amounts of information into manageable sets. It has been applied to a wide variety of research problems and fields, from medicine to psychiatry and archaeology.

Cluster analysis is used also in the development of composite indicators to group information on countries based on their similarity on different individual indicators.

Cluster analysis serves as:(i) a purely statistical method of aggregation of the indicators, ii) a diagnostic tool for exploring the impact of the methodological choices made during the construction phase of the composite indicator,

Cluster analysis Offers a different way to group countries; gives some insight into the structure of the data set.

Various alternative methods combining cluster analysis and the search for a low-dimensional representation have been proposed, focusing on multidimensional scaling or unfolding analysis. Factorial k-means analysis combines k-means

cluster analysis with aspects of FA and PCA. A discrete clustering model together with a continuous factorial model are fitted simultaneously to two-way data to identify the best partition of the objects, described by the best orthogonal linear combinations of the variables (factors) according to the least-squares criterion.

PCA, FA, cluster analysis. Identified subgroups of indicators or groups of countries that are statistically similar.

Cronbach coefficient alpha results for the 23 countries after deleting one individual indicator (standardised values) at a time. 4. 4. Cluster analysis Cluster analysis (CLA) is a collection of algorithms to classify objects such as countries, species,

Various alternative methods combining cluster analysis and the search for a low-dimensional representation have been proposed and focus on multidimensional scaling or unfolding analysis (e g.

A method that combines k-means cluster analysis with aspects of Factor analysis and PCA is offered by Vichi & Kiers (2001.

This is the main difference to Cluster analysis, in which groups are predetermined not. There are also conceptual similarities with Principal Components and Factor analysis

METHODOLOGY AND USER GUIDE ISBN 978-92-64-04345-9-OECD 2008 141 REFERENCES Anderberg M. R. 1973), Cluster analysis for Applications, New york:

Binder D. A. 1978), Bayesian Cluster analysis, Biometrika, 65:31-38. Borda J. C. de (1784), Mémoire sur les élections au scrutin, in Histoire de l'Académie Royale des Sciences, Paris. Boscarino

(2004b), Composite Indicator on e-business readiness, DG JRC, Brussels. Everitt B. S. 1979), Unresolved Problems in Cluster analysis, Biometrics, 35: 169-181.

Massart D. L. and Kaufman L. 1983), The Interpretation of Analytical Chemical Data by the Use of Cluster analysis, New york:

Spath H. 1980), Cluster analysis Algorithms, Chichester, England: Ellis Horwood. Storrie D. and Bjurek H. 1999), Benchmarking European labour market performance with efficiency frontier technique, Discussion Paper FS I 00-2011.

OECD. OECD, 1999, Cluster analysis and Cluster-based Policy in OECD countries, Paris: OECD. Porter, M. 1990), The Comparative Advantage of Nations, New york:

Grouping information on countries 28 3. 2. 1 Cluster analysis 28 3. 2. 2 Factorial k-means analysis 34 3. 3 Conclusions

Cluster analysis can be applied to group the information on constituencies (e g. countries) in terms of their similarity with respect to the different sub-indicators.

The use of cluster analysis to group countries in terms of similarity between different sub-indicators can serve as:(

Cluster analysis could, thereafter, be useful in different sections of this document. The notation that we will adopt throughout this document is the following. tq

2004) Success of software process implementation 3. 2 Grouping information on countries 3. 2. 1 Cluster analysis Cluster analysis (CLA) is given the name to a collection of algorithms used to classify objects

Cluster analysis has been applied in a wide variety of research problems, from medicine and psychiatry to archeology.

cluster analysis is of great utility. 29 CLA techniques can be hierarchical (for example the tree clustering),

Various alternative methods combining cluster analysis and the search for a low-dimensional representation have been proposed, and focus on multidimensional scaling or unfolding analysis (e g.,

A method that combines k-means cluster analysis with aspects of Factor analysis and PCA is presented by Vichi and Kiers (2001.

Cluster analysis, is something of an art, and it is certainly not as objective as most statistical methods.

(or geometric) aggregations or non linear aggregations like the multi-criteria or the cluster analysis (the latter is explained in Section 3). This section reviews the most significant ones. 6. 2. 1 Additive methods The simplest

The hague. 2. Anderberg, M. R. 1973), Cluster analysis for Applications, New york: Academic Press, Inc. 3. Arrow K. J. 1963)- Social choice and individual values, 2d edition, Wiley, New york. 4. Arrow K. J,

Binder, D. A. 1978),"Bayesian Cluster analysis,"Biometrika, 65,31-38.7. Boscarino J. A.,Figley C. R,

Everitt, B. S. 1979),"Unresolved Problems in Cluster analysis,"Biometrics, 35,169-181.37. Fabrigar, L. R.,Wegener, D. T.,Maccallum, R c.,

A Cluster analysis Based on Firm-level Data, Research Policy, 32 (5), 845-863.59. Homma, T. and Saltelli, A. 1996) Importance measures in global sensitivity analysis of model output.

Massart, D. L. and Kaufman, L. 1983), The Interpretation of Analytical Chemical Data by the Use of Cluster analysis, New york:

Spath, H. 1980), Cluster analysis Algorithms, Chichester, England: Ellis Horwood. 132. SPRG (2001) Report of the Scientific Peer review Group on Health Systems Performance Assessment,

Cluster analysis...74 European Competitiveness in KETS ZEW and TNO EN 4 Error! Unknown document property name.

Cluster analysis...125 4. 3. 1. Micro-and Nanoelectronics Europe: The Grenoble cluster...126 4. 3. 2. Micro-and Nanoelectronics Canada:

Cluster analysis...173 5. 3. 1. Industrial biotechnology cluster Europe: Cambridge (United kingdom...174 5. 3. 2. Technology cluster Non-Europe:

Cluster analysis...218 6. 3. 1. Photonics Europe: The Optical Technologies Berlin-Brandenburg cluster (Optecbb...219 6. 3. 2. Photonics Non-Europe:

Cluster analysis...269 7. 3. 1. Advanced Materials Europe: Wallonia's Plastiwin cluster...269 7. 3. 2. Technology cluster non-Europe:

Cluster analysis Nanotechnology has the potential to impact and shape many other industries through its multiple application possibilities.

Cluster analysis Clustering can be viewed from three angles: production locations, research activity and investments indicating future (production) location.

Cluster analysis The geographical distribution of industrial biotechnology clusters can be summarised in four regions: West-and North Europe, American West coast, American East coast, and East asia.

Cluster analysis On a global level, production is located (increasingly in low-cost countries, predominantly in Asia. In 2005 Japan represented 32 percent, Europe 19 percent North america 15 percent, Korea 12 percent and Taiwan 11 percent of world production.

Cluster analysis Advanced materials clusters can be found all over the globe, but mainly in North america, Europe, Japan, Australia,

what other quantitative and qualitative information/methods have informed the strategy (e g. cluster analysis, value chain analysis,

82 3. 8 Cluster analysis: Employees with internet access at their workplace...86 3. 9 Summary and conclusions of ICT and e-business deployment...

We combine this technique with the clustering analysis. Cluster analysis: is advanced an data analysis technique useful to group cases based on their internal similarities.

Sectoral e-Businesswatch (Survey 2007) E-business in the transport & logistics industry 86 3. 8 Cluster analysis:

Employees with internet access at their workplace In this section we perform an advanced cluster analysis on the survey results using the percentage of employees that have internet access at their workplace as the main clustering analysis. With this type of analysis we use the clustering data analysis technique

So with this cluster analysis we detect some adoption and usage differences based mainly to the profiles of activity and positions in the different transport and logistics sectors.

Tools such as cluster analysis have been used successfully for this purpose. Generate and Assemble Ideas. Once the target segment and its core needs are identified,

OECD. OECD, 1999, Cluster analysis and Cluster-based Policy in OECD countries, Paris: OECD. Porter, M. 1990), The Comparative Advantage of Nations, New york:

and they experience stronger growth in adapting open innovation practices than their smaller counterparts 5. 3 Clusters To explore patterns of open innovation among SMES we relied on cluster analysis techniques.

Next, we applied cluster analysis techniques to explore patterns of open innovation practices among SMES. Finally, we used oneway analysis of variance to validate the taxonomy.

In the cluster analysis we combined hierarchical and nonhierarchical techniques. This helps to obtain more stable and robust taxonomies (Milligan and Sokol, 1980;

For each number of clusters (k), we perform a k-means‘nonhierarchical'cluster analysis, in which SMES were divided iteratively into clusters based on their distance to some initial starting points of dimension k

The results of the cluster analysis furthermore show that there are different open innovation strategies and practices among SMES.

Cluster analysis. Oxford university Press, London. Fontana. R.,Geuna, A.,Matt M.,2006. Factors affecting university industry R&d projects:

Cluster analysis in marketing research: review and suggestions for application, Journal of Marketing Research, 20: 134-148.

what other quantitative and qualitative information/methods have informed the strategy (e g. cluster analysis, value chain analysis,

all cluster analysis is restricted to this time period. Table A1. 4. ICT cluster: NACE 2 sector list Sector NACE CODES Comments ICT 261 all (Eurostat definition) 262 all (Eurostat definition) 263 all (Eurostat

and the resulting factors would the input of a cluster analysis. The present analysis also had the aim to investigate the state of planning

adult) were determined by a process of cluster analysis across the metrics outlined in the report (Roa, sales/assets, EBITDA/sales) while also taking into account headcount and absolute revenue.