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Corpus Linguistics and Linguistic Theory 6–1 (2010), 75–103 1613-7027/10/0006–0075 DOI 10.1515/CLLT.2010.004 © Walter de Gruyter Variable scaling in cluster analysis of linguistic data HERMANN MOISL Abstract Where the variables selected for cluster analysis of linguistic data are measured on different numerical scales, those whose scales permit relatively larger values can have a greater infl uence on clustering than those whose scales restrict them to relatively smaller ones, and this can compromise the reliability of the analysis. The fi rst part of this

introduction, the next section provides a brief overview of the multivariate mapping and clustering analysis methods used. Then, a detailed presentation of creating the multivariate maps is given. Finally, results and suggestions are shared in last section. 2 Material and Methods 2.1 Multivariate Mapping Multivariate mapping is the graphic display of more than one variable or attribute of geographic phenomena. The simultaneous display of multiple features and their respective multivariate attributes allows for estimation of the degree or spatial pattern of cross

REFERENCES 1. Global Initiative for Asthma (GINA). Global Strategy for Asthma Management and Prevention. 2016. 2. Mileva Z, Popov T, Staneva M, et al. [Frequency and characteristics of the allergic diseases in Bulgaria.] Allergy and asthma, 2000;5 (Supl.1):3-32 [Bulgarian]. 3. Haldar P, Pavord I, Shaw D, et al. Cluster analysis and clinical asthma phenotypes. Am J Respir Crit Care Med 2008;178:218-24. 4. Moore W, Meyers D, Wenzel S, et al. Identification of asthma phenotypes using cluster analysis in the severe asthma research program. Am J Respir Crit Care Med

References Kaufman L, Rousseeuw P.J. (2005). Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons Krupa M., Witkowicz R., Jacyk G. Opłacalność produkcji w gospodarstwach ekologicznych uczestniczących w polskim FADN, Fragm. Agron . 33 (3) 2016, 46-56. Lampridi, M.G.; Sørensen, C.G.; Bochtis, D. (2019). Agricultural Sustainability: A Review of Concepts and Methods. Sustainability, 11, 5120. Mächler, M., Rousseeuw, P., Struyf, A., & Hubert, M. (2015). Finding Groups in Data: Cluster Analysis Extended. WEB: ftp://128.61.111.11/pub

Volume 5, Issue 1 2009 Article 10 Journal of Quantitative Analysis in Sports Defining the Style of Play in the NHL: An Application of Cluster Analysis Claude B. Vincent, Laurentian University Byron Eastman, Laurentian University Recommended Citation: Vincent, Claude B. and Eastman, Byron (2009) "Defining the Style of Play in the NHL: An Application of Cluster Analysis," Journal of Quantitative Analysis in Sports: Vol. 5: Iss. 1, Article 10. DOI: 10.2202/1559-0410.1133 ©2009 American Statistical Association. All rights reserved. Defining the Style of Play in the

Jahrbücher f. Nationalökonomie u. Statistik (Lucius & Lucius, Stuttgart 2007) Bd. (Vol.) 227/3 Typisierung der Tarifvertragslandschaft Eine Clusteranalyse der tarifvertraglichen Öffnungsklauseln Identifying Types of Flexible Bargaining Agreements Using Cluster Analysis Von Wolf Dieter Heinbach und Stefanie Schröpfer, Tübingen∗ JEL J51, C14 Opening clauses, collective bargaining agreements, cluster analysis, correspondence analysis. Summary The introduction of opening clauses in collective wage agreements allowing firms to deviate from their collective bargaining

References S. Jahirabadkar, P. Kulkarni, ISC- Intelligent Subspace Clustering, A Density Based Clustering Approach for High Dimensional dataset , World Academy of Science, Engineering and Technology, 55, 2009. J. Han M. Kamber, and A. K. H. Tung. Geographic Data Mining and Knowledge Discovery , chapter Spatial Clustering Methods in Data Mining: A Survey, pages 1-29. Taylor and Francis, 2001. B. S. Everitt, Cluster analysis. Edward Arnold, London, 1993. R. Xu and D. C. Wunch, Clustering. John Wiley & Sons, 2009, pp. 263-278. L. Kaufman and P. J. Rousseeuw

of type-2 BMPep will widen the applicability of biomineralization as a method to prepare inorganic nanomaterials. Through our clustering analysis, a more efficient and systematic approach in BMPep selection is possible since previous methods of BMPep classification are unreliable. References 1. Nudelman F, Sommerdijk NA. Biomineralization as an inspiration for materials chemistry. Angew Chem Int Ed 2012;51:6582–96. 10.1002/anie.201106715 2. Mann S. Biomineralization: principles and concepts in bioinorganic materials chemistry. UK: Oxford University Press, 2001. 3