clustering

Clustering

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Clustering (199)
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and Pontieri (2012) propose a predictive clustering approach in which context-related execution scenarios are discovered

Clustering: In a clustering step the different individual models are grouped in a way such that models within one group are similar

and models belonging to different groups are different. Here, typical techniques of cluster analysis or multivariate statistics can be used.

In a first step, clustering techniques are used to identify and reconstruct the given model groups. Since the model repository consists of 80 single models with 8 different processes and 10 variants each,

Due to the clustering of process instances this person can work fulltime as a Process Manager and the professionalism (i e.,

, 263 268,270 272,287 clustering, 161 162,170 collection, 170,216 corpus, 171 harmonization, 164 integration, 60,162 matching, 171 merge, 238,269 similarity, 170 Process

Entrepreneurial Networks & Mentoring 49 6. Access to Markets 50 6. 1 First time Exporters 50 6. 2 Clustering Programme 51 6. 3 Public

and awareness events and are supported in the development of a market plan for their priority target market. 6. 2 Clustering Programme Enterprise Ireland's pilot clustering programme was established in 2012 to encourage groups of businesses to collaborate to achieve specific business objectives,

Others established local production facilities to benefit from the gradually emerging clustering tendencies and from the availability of skilled workforce.

However, measures that promoted clustering and the improvement of clusters'services portfolio, were announced last in 2012 in WT.

Past policy instruments including clustering; supplier development (support to indigenous companies'investment in new technology to make them capable to become multinational subsidiaries'suppliers)

Although the region was among the first ones where bottom-up clustering tendencies were identified by regional economics researchers,

policy-makers consider it important to enhance regional clustering tendencies (intensify collaboration among stakeholders) and facilitate existing clusters'accreditation process.

and the activity it has carried out in the framework of SEE IDWOOD programme (Clustering, knowledge, innovation and design in SEE wood sector).

EI) 143 Build on Phase One of the Pilot Industry-led clustering initiative involving fifty companies by implementing the recommendations of the clustering Review carried out in 2014.

K-means for clustering TAI countries...78 Table 14. Normalisation based on interval scales...83 Table 15.

or when is believed it that some of them do not contribute to identifying the clustering structure in the data set,

and then apply a clustering algorithm on the object scores on the first few components,

as PCA or FA may identify dimensions that do not necessarily help to reveal the clustering structure in the data

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.

if the classification has an increasing number of nested classes, e g. tree clustering; or nonhierarchical when the number of clusters is decided ex ante,

e g. k-means clustering. However, care should be taken that classes are meaningful and not arbitrary or artificial.

including Euclidean and non-Euclidean distances. 18 The next step is to choose the clustering algorithm,

METHODOLOGY AND USER GUIDE ISBN 978-92-64-04345-9-OECD 2008 77 A nonhierarchical method of clustering,

is k-means clustering (Hartigan, 1975). This method is useful when the aim is to divide the sample into k clusters of the greatest possible distinction.

k-means clustering (standardised data. Table 13. K-means for clustering TAI countries Group1 (leaders) Group 2 (potential leaders) Group 3 (dynamic adopters) Finland Netherlands Sweden USA Australia

Canada New zealand Norway Austria Belgium Czech Rep. France Germany Hungary Ireland Israel Italy Japan Korea Singapore Slovenia Spain UK Finally, expectation

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

Haq M. 1995), Reflections on Human Development, Oxford university Press, New york. Hartigan J. A. 1975), Clustering Algorithms, New york:

Heiser, W. J. 1993), Clustering in low-dimensional space. In: Opitz, O.,Lausen, B. and Klar, R.,Editors, 1993.

by further developing instruments that allow for aggregation of local initiatives, such as clustering, to really unlock the potential of innovative SMES.

such as clustering, to unlock the potential of innovative SMES. This constraint reflects a more general situation,

by further developing instruments that allow for aggregation of local initiatives, such as clustering, to really unlock the potential of innovative SMES. 2. 2. 2 A coordinated,

This overview consists of a clustering of the funded CAPS projects under 14 emerging categories.

The clustering is based on available public documents of CAPS projects and on the knowledge available among the authors,

This clustering considers the main'innovations'produced by the projects. More comprehensive outputs of each project will then comprise the ways in

This overview consists of a clustering of the funded CAPS projects under 14 emerging categories.

The clustering is based on available public documents of CAPS projects and on the knowledge available among the authors,

This clustering considers the main'innovations'produced by the projects. More comprehensive outputs of each project will then comprise the ways in

A provisional thematic clustering of DSI organisations is emerging, grouping activities into 6 macro clusters that capture the way DSI is growing and developing:(

In detail, there is a clustering coefficient of. 887, signalling a fairly high density of interconnections in existing communities (Latapy, 2008).

The way to interpret a clustering coefficient is that it is the measurement of how likely it is that the organisations linked to each other are linked also.

a provisional thematic clustering of DSI organisations is emerging, grouping activities into 5 macro clusters that capture the way DSI activities affect

New clustering and categories will then emerge from the empirical data. Within each community, there will be certain organisations that have a high centrality, the movers and shakers of social innovation.

a provisional thematic clustering of DSI organisations is emerging, grouping activities into 5 macro clusters that capture the way DSI activities affect

Clustering is particularly important to gain access to new ideas and tacit knowledge, especially in young industries.

In a clustering strategy, firms take advantage of linkages with other enterprises afforded by geographic proximity

Prevenzer, Martha, 1997,‘The Dynamics of Industrial Clustering in Biotechnology,'Small Business Economics, 9 (3), 255-271.

Investigation of clustering impact to SMES'innovation in Indonesia. Paper presented at the 2nd International Conference on International Business (ICIB), University of Macedonia.

Firm clustering and innovation: Determinants and effects. Papers in Regional Science, 80,337-356. Pérez-Luño, A.,Wiklund, J,

Clustering and Industrialization: Introduction. World Development, 27 (9), 1503-1514. Schoales, J. 2006. Alpha Clusters:

Innovation and Clustering in the Globalised International Economy. Urban Studies, 41 (5/6), 1095-1112.

whether their=clustering‘has fostered a more collaborative culture of learning and knowledge exchange. While in technology parks there is a relatively high level of collaboration with universities

RETA expresses the more widely held belief that clustering of high technology firms, described as Andalusia‘s=closeness‘model is the most effective means of offering support to fast growing and technology dynamic SMES.

whether their=clustering‘has fostered a more collaborative culture of learning and knowledge exchange. A recent study that explored the type

For example, the policy of encouraging clustering of SMES in technology parks and industrial estates is informed by recent research that highlights the importance of encouraging proximity between firms in the pursuit of innovation.

encouraging the physical clustering and co-presence of firms (as we have shown) is not in itself,

the underlying rationale being that clustering of technology-intensive firms enhances their growth and expansion.

whether=clustering‘has fostered a more collaborative culture of learning and knowledge exchange. Indeed few firms appear to develop collaborations with other firms co-located in the same park.

and sectoral mix of cluster strategies Business clustering has brought significant advantages for smaller firms especially because of knowledge spillovers from one firm to another or from institutions to firms.

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

or nonhierarchical when the number of clusters is decided ex ante (for example the k-means clustering).

To do so the clustering techniques attempt to have more in common with own group than with other groups, through minimization of internal variation while maximizing variation between groups.

the next step is to choose the clustering algorithm, i e. the rules which govern how distances are measured between clusters.

3 6 9 12 15 18 21 Figure 3. 3. Linkage distance versus fusion step in the hierarchical clustering for the technology achievement example.

A nonhierarchical method of clustering, different from the Joining (or Tree) clustering shown above, is the k-means clustering (Hartigan,

1975). ) This method is useful when the aim is that of dividing the sample in k clusters of greatest possible distinction.

Table 3. 8. K-means clustering for the 23 countries in the technology achievement case study Group1 (leaders) Group 2 (potential leaders) Group 3 (dynamic adopters

kmeans clustering (standardized data. Finally, expectation maximization (EM) clustering extends the simple k-means clustering in two ways:

On the other hand, the relationships within a set of objects (e g. countries) are explored often by fitting discrete classification models as partitions, n-trees, hierarchies, via nonparametric techniques of clustering.

or when is believed it that some of these do not contribute much to identify the clustering structure in the data set,

frequently carrying out a PCA and then applying a clustering algorithm on the object scores on the first few components.

because PCA or FA may identify dimensions that do not necessarily contribute much to perceive the clustering structure in the data and that,

A discrete clustering model together with a continuous factorial one are fitted simultaneously to two-way data,

Hartigan, J. A. 1975), Clustering Algorithms, New york: John Wiley & Sons, Inc. 53. Harvey A.,(1989), Forecasting, structural time series models and the Kalman filter.

While regional or national clustering has certainly its merits and can be an important driver for advance in nanotechnology,

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

but also to local and national governmental initiatives that promote regional clustering activities (Sydow et al.,2007).

Nordicity Group (2006), Regional/local industrial clustering: Lessons from abroad, Ottawa: National Research Council Canada.

Historical Evolution of Creative Clusters‘Clustering'is a term that can be applied to a variety of human, animal, biological and scientific states.

Within this clustering the university and business enterprise may be supplemented by an incubation partner, typically separate from the main university campus and on the outskirts of the university town or city.

which already demonstrate a‘clustering'effect where there is a strong base of companies and research activity (e g.

A provisional thematic clustering of DSI organisations is emerging, grouping activities into 6 macro clusters that capture the way DSI is growing and developing:(

Several studies illustrate processes of regional clustering, analysing in some detail how initial conditions within a region are triggered reinforced,

thus introducing an interaction-based clustering process (Wolter 2004) 9. Initially, high-tech industries and clusters need triggers to set them off onto technology specific trajectories.

In contrast to the development of territories such as the Silicon valley, Route 128 or the Research Triangle Park in North Caro-9 Wolter (2004) distinguishes two dominant explanations for clustering tendencies,

and spatial clustering allows firms to realise and draw on knowledge spillovers. With this classification we aim at capturing the regional RTD context.

'The author indicates that there is also evidence showing that embedded relationships may be a relic of the past instead of being a result of clustering tendencies. 5. 3. Unsolved Questions in Regional RTD All this poses additional challenges to be taken into account by policy-makers.

Territorial Clustering and High-technology Innovation: From Industrial Districts to Innovative Milieux. ESRC Centre for Business Research working paper 54, University of Cambridge.

towards a knowledgebased theory of spatial clustering. Environment and Planning A 34,429-449. Malmberg, A.,Ö. Sölvell and I. Zander (1996:

Spatial Clustering, Local Accumulation of Knowledge and Firm Competitiveness. Geografiska Annaler, 78 B (2), 85-97.

Knowledge-based industrial clustering: International comparisons. In: J. de la Mothe and G. Paquet (eds.:

High-tech industry clustering rationales: the case of German biotechnology. In P. Cooke and A. Piccaluga (eds.:

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.

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

In the following table we have the clustering algorithm results for the variable Percentage of employees that have internet access at their workplace.

Clustering is particularly important to gain access to new ideas and tacit knowledge, especially in young industries.

In a clustering strategy, firms take advantage of linkages with other enterprises afforded by geographic proximity

Prevenzer, Martha, 1997,‘The Dynamics of Industrial Clustering in Biotechnology,'Small Business Economics, 9 (3), 255-271.

The clustering I chose for the parameters in the intended model is based on the understanding that the parameters in each subset are interconnected closely.

and cooperation Cluster management MINI-EUROPE Cluster Support Environment Model Clustering physical infrastructure requirements to facilitate growth

and internationalisation(§3. 2. 5). Medium Cluster policies SMART+SMESGONET Clustering management activities supporting the internationalisation

The economics of R&d location (indivisibility, strong spatial clustering of innovation activities) makes regional responses to R&d globalisation naturally appropriate.

Commercial vehicle to drive Innovation Further Investigation of Clustering Part-time Innovation drivers Development of more Innovation Centres Thank you for your Time

The traditional clustering of firms per type of material has been replaced by clustering around market demand.

traditional clusters became part of a larger clustering of firms and the speed of innovation became even faster (Resp. 15;

The importance of clustering in economic development has changed radically the past decades for various reasons.

In this study clustering correlates significantly and negatively with innovation. The relationship is linear (a=0. 017, ß=-0. 476, r2=0. 0489.

then poverty-driven clustering no longer has a significant impact on innovation et al (see next section).

Tambunan (2006b) reports other studies in Indonesia where clustering has no significant impact either. IHS Working Paper 27.2013.

Promoting small and medium entreprises with a clustering approach: A policy experience from indonesia. Journal of Small Busines Management 43 (2): 138-54.

18 Centrality and clustering...21 Countries'positions in the network...25 Core and periphery of the R&d network...

Further measure of a node's position in the network used in this study relates to the extent of clustering between nodes.

This property of a network structure can by captured by the clustering coefficient (Watts and Strogatz 1998

In directed networks, the clustering coefficient cc i C of node i is defined as:((1)) i icc i i k ke C (13) where ki is the degree of Vi

The clustering coefficient of a node is always a number between 0 and 1, where for a fully connected network CC=1. International R&d centres as a network A straightforward way of representing international R&d centres as a network is through drawing a line connecting two countries that share an R&d centre

7 Average in-strength 141 Average out-strength 58 Closeness centralization 0, 107 Betweenness centralization 0, 038 Clustering centralization 0, 457 Source:

Centrality and clustering Turning to other measures of the network, we first consider the measure of closeness centrality.

Turning to the clustering centrality, it is worth noting that an analysis of this measure can be found, for example,

because networks with strong clustering properties are likely to reflect some strong geographical structure in which short-distance links count more than long-distance ones.

In the context of the R&d network, the value of clustering coefficient is 0. 46,

This type of clustering behaviour lets us conclude that'local'links tend to play an important role.

Furthermore, the negative skew of the clustering coefficient's distribution indicates that most of the countries tend to 0 100 200 300 Frequency 0. 02.04.06.08.1 Betweenness centrality Frequency kdensity betweennesscentrality 24 be members

Clustering coefficient distribution Note: The probability density function was estimated using the kdensity estimation procedure, i e. univariate kernel density estimation.

when we look at the relationship between the clustering coefficient and nodes'degree and strength (see Table 11).

where the Pearson 0 5 10 15 Frequency 0. 2. 4. 6. 8 1 Clustering coefficient Frequency kdensity clusteringcoefficient 25 correlation value is either close to or above 0

i e. closeness centrality and clustering coefficient, the problem of multicolinearity seems to be of lesser importance.

This type of clustering reveals that there are strong'local'links, which however do not imply geographical or cultural proximity,

2 Closeness centrality 0, 496*1 3 Clustering coefficient 0, 322*-0, 097 1 4 In-degree 0, 796*0, 465

This may take the form of direct funding of research, public procurement or support for clustering,

Strong clustering of ICT activity Larger areas of intensive ICT activities, sometimes including a 1st tier region,

Next, an internal procedure was undertaken by the SCALE CCR research team to conduct a clustering and further reduction of the recommendations.

A national clustering policy is essential to provide support and structure to cluster development. Additionally, there is a requirement for policies to ensure the creation of the type of environment that companies need

and public awareness in the areas of cluster structure, cluster development and the regional benefits of clustering;

A provisional thematic clustering of DSI organisations is emerging, grouping activities into 6 macro clusters that capture the way DSI is growing and developing:(

and could also reinforce clustering around the richest industrial and urban areas, and increase the economic and social disparities between urban and rural populations.

Gradually, the clustering effect took place. Therefore, it is not a totally top-down system. As the second-largest University City in France, Lyon has sufficient supply of talent in the creative industries.

Clusteranalysisrevealedthreegroupsofsmes, clustering firms intogroupswithsimilaropeninnovationpractices. Theirfeaturesconfirm Lichtenthaler's (2008) conclusionthat companies seldomfocusoneithertechnologyexploitationor technologyexploration. Rather, openinnovatingcompanies tend tocombinethesetwoaspectsofopeninnovation.

since the addition of irrelevant variables can have a serious effect on the results of the 25 clustering (Milligan and Cooper, 1987).

as a way to reduce the number of dimensions to be used in the clustering. In general

and therefore no variable would implicitly be weighted more heavily in the clustering and thus dominate the cluster solution (Hair et al.,

clustering methods. Applied Psychological Measurement 11,329-354. Milligan, G. W.,Sokol, L. M.,1980. A two-stage clustering algorithm with robust recovery characteristics.

Diversification and clustering of SMES for future growth...74 5. 5 Israel: Envisaged targeted support for high-growth sectors and SMES...

The strategic line of SME policy discussion in 2010 gravitated around the diversification and clustering of SME business activities.

Clustering policy initiatives focus on promoting local clusters, such as regional linkages among manufacturers, and university industry collaborations.

issues of human capital, access to specialised technology and business consulting, R&d clustering, technology scouting to identify R&d projects with commercial potential, technology transfer,

development and innovation. 125 The relationship between internationalisation and clustering may be of particular interest, since local clusters are seen often as breeding grounds for innovation.

and national level. 126 One could assume that clustering and internationalisation mutually reinforce each other. 127 However,

While the determinants of success of clusters and the relationship between clustering and internationalisation cannot be dealt with in depth in this Policy Brief,

and practical tools in Europe is also considering the links between clustering and internationalisation; see http://www. proinno-europe. eu/tactics. 128 Dahl Fitjar/Rodríguez-Pose (2011),

In Japanese government's SME policies, the strategic line of discussion gravitates around the diversification and clustering of SME business activities.

Diversification and clustering of SMES for future growth Summary Although the fall out from the 2008 Lehman brothers collapse continues to skew the Japanese government's SME (small and medium-sized enterprise) policies towards finance and employment safety net issues,

the strategic line of discussion in 2010 gravitates around the diversification and clustering of SME business activities.

Clustering policy initiatives focus on promoting (1) local clusters, such as regional linkages among small and medium manufacturers,

Through these overlapping diversification and clustering policy initiatives, the government's 2009 New Growth Strategy (Basic Policies) Toward a Radiant Japan identifies SMES as an engine for future high economic growth.