Data mining

Clustering (193)
Data mining (33)
Information mining (3)
Text mining (16)
Web mining (4)

Synopsis: Ict: Data: Data mining:

for example, meanwhile allow for real-time mining of business processes based on the digital traces that single process steps leave or based on text mining possibilities (Guâ nther, Rinderle-Ma, Reichert, Van der

Replaying history on process models for conformance checking and performance analysis. WIRES Data mining and Knowledge Discovery, 2 (2), 182†192.

Data mining and Knowledge Discovery, 14 (2), 245†304. Barros, A.,Decker, G.,Dumas, M, . & Weber, F. 2007).

IEEE Symposium on Computational Intelligence and Data mining (CIDM 2013)( pp. 127†134. Singapore: IEEE. Montahari-Nezhad, H.,Saint-paul, R.,Casati, F,

IEEE Symposium on Computational Intelligence and Data mining (CIDM 2011)( pp. 184†191. Paris: IEEE. OMG.

IEEE Symposium on Computational Intelligence and Data mining (CIDM 2011)( pp. 148†155. Paris: IEEE. Weijters, A,

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

In Proceedings of the IEEE symposium on computational intelligence and data mining (CIDM)( pp. 111†118.

In Proceedings of the SIAM international conference on data mining (SDM)( pp. 644†655. SIAM. 154 M. Dumas and F. M. Maggi Identification of Business Process Models in a Digital World Peter Loos, Peter Fettke, Juâ rgen Walter, Tom Thaler,

•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.

The modelsynset created in phase 3 can support the grouping. Known similarity measures for enterprise models can also be applied (Dijkman et al.

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 profession-alism (i e.,

a collaborative research center that gathers ten Estonian IT organizations with the aim of conducting industry-driven research in service engineering and data mining. From 2000 to 2007,

/modeling, 13,60, 63†66,78, 108, 177†189,225, 263†268, 270†272,287 clustering, 161†162,170 collection, 170,216 corpus, 171 harmonization, 164

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,

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.

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,

G#2v 7783 Data mining G#3v 7784 Clustering G#4v 7785 Cluster analysis 0#5#cluster analysis Cluster analysis

G#4v 7786 Clustering 0#5#clustering Clustering G#3v 7787 Data mining 0#4#data mining Data mining

G#3v 7788 Information mining 0#4#information mining Information mining G#3v 7789 Text mining 0#4#text mining Text mining

G#3v 7790 Web mining 0#4#web mining Web mining G#2v 7791 Data recovery 0#3#data recovery Data recovery

G#3v 7792 Uflysoft data recovery 0#4#uflysoft data recovery Uflysoft data recovery G#2v 7793 Data sharing 0#3#data sharing Data sharing

G#3v 7794 File sharing 0#4#file sharing File sharing G#2v 7795 Data storage devices G#3v 7796 Data storage device

0#4#data storage device Data storage device G#3v 7797 Sim G#4v 7798 Micro sim 0#5#micro sim Micro sim

G#4v 7799 Sim 0#5#sim Sim G#4v 7800 Sim card 0#5#sim card Sim card

G#2v 7801 Data stream 0#3#data stream Data stream G#2v 7802 Data structure 0#3#data structure Data structure

G#2v 7803 Data transmission 0#3#data transmission Data transmission 0#3#digital communication Data transmission 0#3#digital transmission Data transmission

G#2v 7804 Data type 0#3#data type Data type G#2v 7805 Data warehouse 0#3#data centre Data warehouse

0#3#data repository Data warehouse 0#3#data warehouse Data warehouse G#3v 7806 Information center 0#4#information center Information center

G#2v 7807 Database G#3v 7808 Database 0#4#data base Database 0#4#database Database

G#3v 7809 Database management system 0#4#database management system Database management system G#3v 7810 Database systems G#4v 7811 Adabas

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, who are also part of the CAPS community. It has to be noted that CAPS projects are still in the early stage of development, therefore a

This clustering considers the main 'innovations'produced by the projects. More comprehensive outputs of each project

E-health addresses new healthcare trends by facilitating individual information capture, data mining, and concomitant access by multiple stakeholders..

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, who are also part of the CAPS community. It has to be noted that CAPS projects are still in the early stage of development, therefore a

This clustering considers the main 'innovations'produced by the projects. More comprehensive outputs of each project

-matic clustering of DSI organisations is emerging, grouping activities into 6 macro clusters that capture the way

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

to interpret a clustering coefficient is that it is the measurement of how likely it is that the organisations

to date, a provisional thematic clustering of DSI organisations is emerging, grouping activities into 5 macro

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 â€oemovers and

to date, a provisional thematic clustering of DSI organisations is emerging, grouping activities into 5 macro

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 â€oemovers and

However, it has to be kept in mind that data mining is not only a tool for the surveillance of citizens and

data mining create tensions between citizens and governments Technological progress and growing degree of interconnectedness, growing

and Newton Minow,"Government Data mining,"2008 Cave, Jonathan, C Van Oranje, Rebecca Schindler, Alaa Shehabi,

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

â'Clustering. Groups of enterprises working in the same product are seedbeds for the exchange of new ideas.

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

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

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

Firm clustering and innovation: Determinants and effects. Papers in Regional Science, 80,337-356 PÃ rez-Luã o, 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

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

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

clustering and co-presence of firms (as we have shown) is not in itself, sufficient to encourage higher

instruments for the promotion of business modernisation, the underlying rationale being that clustering of technology-intensive firms enhances their growth and expansion.

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...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

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.

While regional or national clustering has certainly its merits and can be an important driver for advance in nanotechnology, a too strong national focus may understate

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

In terms of production output Asia is the largest geographical agglomeration with China accounting for 27 percent of production in

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

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

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

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

 clustering  manifests  itself  in  cities  and  towns  across

 clustering  the  university  and  business  enterprise  may  be

and sectoral diversity and/or clustering Skills & Education an analysis of the skills, educational attainment and education resources

and sectoral diversity and/or clustering Skills & Education an analysis of the skills, educational attainment and education resources

-matic clustering of DSI organisations is emerging, grouping activities into 6 macro clusters that capture the way

information/methods have informed the strategy (e g. cluster analysis, value chain analysis, peer review, foresight 2. 4 Does the document propose a vision for the region?

regional clustering, analysing in some detail how initial conditions within a region are reinforced, what triggered cluster processes and how innovative clusters of firms

thus introducing an interaction-based clustering proc -ess (Wolter 2004) 9. Initially, high-tech industries and clusters need triggers to set them

-9 Wolter (2004) distinguishes two dominant explanations for clustering tendencies, namely resource-based ones, emphasizing the access to resources,

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

-sult 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 In -novative Milieux. ESRC Centre for Business Research working paper 54, University of Cambridge

-based theory of spatial clustering. Environment and Planning A 34,429-449 Malmberg, A.,Ã. SÃ lvell 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.:

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. With this technique one can build groups of cases (companies) with a similar profile based on some relevant indicators or

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 to group the different transport and logistics sector companies in order to have three different clusters of similar companies based on the percentage of employees that

In the following table we have the clustering algorithm results for the variable â€oepercentage of employees that have internet access at their workplaceâ€.

It then applies text mining, epidemiological and other computational techniques to assess and detect †signalsâ€

Business intelligence and in particular Data mining are useful tools in the detection of outbreaks when used for the real time detection of infection trends within hospitals

member states through the use of Business intelligence and Data mining for real time detection of in-hospital infections.

ï A cryptographic technology for privacy preserving data mining of patient health data to support clinical research and knowledge creation for clinical decision sup

methods and text mining techniques 15 In summary, several efforts have been devoted to the definition of methods for

Use of text mining to predict patient compliance. SAS Global Forum, Proquest 2008 16. Banescu, S.,Zannone, N.:

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, the

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

â'Clustering. Groups of enterprises working in the same product are seedbeds for the exchange of new ideas.

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

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

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.

Data mining with neural networks: Solving business problems from application development to decision support. Mcgraw-hill, Inc. Hightstown, New

Clustering physical infrastructure requirements to facilitate growth and internationalisation (§3. 2. 5 Medium Cluster policies SMART+SMESGONET Clustering management activities supporting the

internationalisation and R&d cooperation (§3. 2. 5 Medium Cluster internationalisation DISTRICT+Cluster and Foreign

spatial clustering of innovation activities) makes regional responses to R&d globalisation naturally appropriate. Indeed there has been a trend over the past decades to devolve competences for innovation

text mining and programming procedures. Regionalization of the Scopus data is even more cumbersome given the lower quality of the address information in this database

hand, and term frequencies originated from text mining, on the other hand, are usually not sufficient at this level if applied alone.

•Further Investigation of Clustering •Part-time Innovation drivers •Development of more Innovation Centres

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

Table 1: survey sample per subsector and firm size (turn over Firm-size W ood Pottery

became part of a larger clustering of firms and the speed of innovation became even faster (Resp

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 (Î=0. 017, Î=-0. 476, r2=0. 0489.

controlled for absorptive capacity and position in value chain, then poverty-driven clustering no longer has a significant impact on innovation et al (see next section.

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

proposed data mining and data collection taxes, directed specifically at large internet companies such as Google and Facebook. 125 Higher taxes on ICT-producing companies

Jacob Albert, â€oefrance Wants to Tax Data mining, and It†s Not a Bad Idea, †Quartz

clustering and further reduction of the recommendations. As the framework for clustering the recommendations in key areas of policy actions, the CCR multidimensional concept was used

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

development and the regional benefits of clustering •To carry out the necessary prioritisation research and analysis in the Midwest Region to identify

revenue, and has used data mining to achieve a fourfold improvement in churn forecasting. The operator found that customers planning to quit

skills in the areas of data mining, statistics and domain expertise, as well as data preparation cleaning and visualization.

-matic clustering of DSI organisations is emerging, grouping activities into 6 macro clusters that capture the way

and data mining that allow iden -tifying patterns of behaviour and usages. see the latest version of the three description tables pre

To explore patterns of open innovation among SMES we relied on cluster analysis techniques. These are sensitive to the selection of the variables used, since the

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 clustering. In general, PCA reduces the risk that single indicators dominate a cluster solution,

weighted more heavily in the clustering and thus dominate the cluster solution (Hair et al.,

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

-hierarchical†cluster analysis, in which SMES were divided iteratively into clusters based on their distance to some initial starting points of dimension k. While some k

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

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

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

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

5. 3. Cluster analysis To explore the incidence of open innovation in more detail, we decided to cluster the respondents in groups of

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

Cluster analysis revealed three groups of SMES, clustering ï rms into groups with similar open innovation practices

relatively more medium-sized companies, the clustering implicitly suggests a sequence in the adoption of open

Cluster analysis revealed three homogeneous groups of SMES with similar application of open innovation practices. The clusters implicitly suggest a

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

Cluster analysis Motives and challenges Discussion Conclusions Limitations Suggestions for further research Principal component analysis References

Technovation 29 (2009) 423 m P. J R e L y Re s, H Tech

5. 3. Cluster analysis To explore the incidence of open innovation in more detail, we decided to cluster the respondents in groups of

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

Cluster analysis revealed three groups of SMES, clustering ï rms into groups with similar open innovation practices

relatively more medium-sized companies, the clustering implicitly suggests a sequence in the adoption of open

Cluster analysis revealed three homogeneous groups of SMES with similar application of open innovation practices. The clusters implicitly suggest a

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

Cluster analysis Motives and challenges Discussion Conclusions Limitations Suggestions for further research Principal component analysis References

WARN-Count in xref table is 0 at offset 11669896 Knowledge Partner Research Report OPEN INNOVATION IN SMES

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

clustering of SME business activities. Diversifica -tion policy initiatives focus on supporting SMES to move (1) up the technology ladder,(2) between

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, and an

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

-pean and national level. 126 One could assume that clustering and internationalisation mutually reinforce each

clusters and the relationship between clustering and internationalisation cannot be dealt with in depth in this Policy Brief, this finding strongly points to the importance of internationalisation for innovation

-tween clustering and internationalisation; see http://www. proinno-europe. eu/tactics 128 Dahl Fitjar/Rodrã guez-Pose (2011), p. 5

and clustering of SME business activities. Diversification policy initiatives focus, among other items, on sup -porting SMES to move between industries (new business activities in related industries) â€

Diversification and clustering of SMES for future growth Summary Although the fall out from the 2008 Lehman brothers collapse continues to skew the Japa

diversification and clustering of SME business activities. Diversification policy initiatives focus on supporting SMES to move (1) up the technology ladder (product development),

Clustering policy initiatives focus on promoting (1) local clusters, such as regional linkages among small and medium manufacturers, and (2) network

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

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

clustering of SME business activities. Diversifica -tion policy initiatives focus on supporting SMES to move (1) up the technology ladder,(2) between

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, and an

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

-pean and national level. 126 One could assume that clustering and internationalisation mutually reinforce each

clusters and the relationship between clustering and internationalisation cannot be dealt with in depth in this Policy Brief, this finding strongly points to the importance of internationalisation for innovation

-tween clustering and internationalisation; see http://www. proinno-europe. eu/tactics 128 Dahl Fitjar/Rodrã guez-Pose (2011), p. 5

and clustering of SME business activities. Diversification policy initiatives focus, among other items, on sup -porting SMES to move between industries (new business activities in related industries) â€

Diversification and clustering of SMES for future growth Summary Although the fall out from the 2008 Lehman brothers collapse continues to skew the Japa

diversification and clustering of SME business activities. Diversification policy initiatives focus on supporting SMES to move (1) up the technology ladder (product development),

Clustering policy initiatives focus on promoting (1) local clusters, such as regional linkages among small and medium manufacturers, and (2) network

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

clustering of businesses and firms, including those involved in interrelated activities and in high growth

) Nine indicative locations in the Southeast Region have been identified with potential for clustering by the Marine Institute in its report †Development Strategy for Marine and Leisure Infrastructure†and the relevant

The need for clustering of potential customers of information technology infrastructure to provide a basis on

informed the strategy (e g. cluster analysis, value chain analysis, peer review, foresight 2. 4 Does the document propose a vision for the region?

and clustering to achieve benefits of scale will increase regional research competitiveness. This should serve to increase regional

and clustering to achieve benefits of scale will increase regional research competitiveness. This should serve to increase regional

intelligence, WEB technologies (web mining), robotics, integrated systems, producing systems and planning production systems, calculation systems, voice recognition, imagesâ€

some minor clustering †as the smart specialization priorities of the strategy As a whole, the selection process briefly described above

available only for the 2008-2010 period, all cluster analysis is restricted to this time period Table A1. 4. ICT cluster:

ï Cross-clustering capacity, entrepreneurship and the innovation capabilities of SMES and other strategic actions should be defined

reinforcing some priority clustering processes in a similar way than in some European regions c) Try to increase the participation of different private agents and social leaders in the process

i) Strategic actions fostering cross-clustering capacity, entrepreneurship and the innovation capabilities of SMES should be defined

Italy (the spatial clustering analysis and annexes in Chapter 3) and Andrea Piccaluga, Scuola Superiore Sant†Anna, Pisa, Italy,

The geographical clustering of knowledge-intensive activities...136 The role of local knowledge flows for spatial agglomerations

There is strong spatial clustering in knowledge-driven sectors, i e. those where R&d intensity basic university research and highly-skilled workers are most important.

The geographical clustering of knowledge-intensive activities Activities can cluster for different reasons, such as availability of intermediate suppliers

findings underline the importance of knowledge-driven clustering in knowledge-intensive industries. They are reflected also in the results of a recent OECD study of seven

The above section illustrated the phenomenon of spatial clustering of economic activity in knowledge intensive sectors.

suggests that local knowledge transfers are important to this clustering process. This literature stresses the fact that knowledge does not spill over long distance †which means

exists between knowledge spillovers, spatial clustering and innovative output (Giuliani 2005). ) This is especially true for knowledge-driven sectors,

In the case of uneven spatial clustering global spatial indicators, such as Moran†S i, are found to be less useful and local indicators

suggest clustering of similar values (positive spatial correlation), whereas the HL and LH locations indicate spatial outliers (negative spatial correlation

The geographical clustering of knowledge-intensive activities European union Figure 3. 1. Distribution of HTM firms in the European union (Quantiles based on LQS

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