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Books on the topic 'Statistical cluster analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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1

McEwan, J. A. Cluster analysis and preference mapping. Chipping Campden: Campden & Chorleywood Food Research Association, 1998.

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2

Statistical methods for disease clustering. New York: Springer, 2010.

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3

J, Hayes Richard, and Hayes Richard J. Cluster randomised trials. Boca Raton: Taylor & Francis, 2009.

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4

D, Nagel, and Sator H, eds. Cluster analysis in clinical chemistry: A model. Chichester: Wiley, 1987.

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5

Brown, Jennifer A. The relative efficiency of adaptive cluster sampling for ecological surveys. Palmerston North, N.Z: Faculty of Information and Mathematical Sciences, Massey University, 1996.

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6

Canada, Canada Agriculture, ed. Optimal set covering for biological classification. Ottawa: Agriculture Canada, 1993.

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7

Stahl, Herbert. Clusteranalyse grosser Objektmengen mit problemorientierten Distanzmassen. Thun: H. Deutsch, 1985.

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8

Das Problem der Distanzbindungen in der hierarchischen Clusteranalyse. Frankfurt am Main: P. Lang, 1995.

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9

Dimpsey, Robert Tod. Performance analysis of the Alliant FX/8 multiprocessor using statistical clustering. Urbana, IL: University of Illinois at Urbana-Champaign, 1988.

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10

Neil, Klar, ed. Design and analysis of cluster randomization trials in health research. London: Arnold, 2000.

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11

Stahl, Herbert. Clusteranalyse grosser Objektmengen mit problemorientierten Disranzmassen. Thun: H. Deutsch, 1985.

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12

Structure in complex networks. Berlin: Springer, 2009.

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13

library, Wiley online, ed. Cluster Analysis. 5th ed. Hoboken: Wiley, 2011.

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14

Murtagh, Fionn. Handbook of cluster analysis. Edited by Meilă Marina 1962 editor. Boca Raton: Taylor & Francis, 2016.

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15

Stata multivariate statistics reference manual: Release 12. College Station, Tex: StataCorp LP, 2011.

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16

Keefe, Ryan. Resource-constrained spatial hot spot identification. Santa Monica, CA: RAND, 2011.

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17

1965-, Sullivan Thomas, ed. Resource-constrained spatial hot spot identification. Santa Monica, CA: RAND, 2011.

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18

Haskey, John. The OPCS socio-economic area classifications: An evaluation of their effectiveness for vital statistics. (London): OPCS, 1985.

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19

Sutradhar, Brajendra C. Dynamic mixed models for familial longitudinal data. New York: Springer, 2011.

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20

Ciok, Alicja. On the number of clusters--a grade approach. Warszawa: Instytut Podstaw Informatyki Polskiej Akademii Nauk, 2004.

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21

Stata multivariate statistics reference manual: Release 11. College Station, Tex: StataCorp LP, 2009.

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22

Malyshev, V. A. Gibbs random fields: Cluster expansions. Dordrecht [Netherlands]: Kluwer Academic Publishers, 1991.

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23

International Federation of Classification Societies. Conference. Classification, clustering and data analysis: Recent advances and applications. Berlin: Springer, 2002.

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24

International Federation of Classification Societies. Conference. Program and abstracts: IFCS 2002 : data analysis, classification, and related methods : 8th Conference of the International Federation of Classification Societies, July 16-19, 2002, Cracow, Poland. Cracow: University of Economics Publishers, 2002.

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25

DIANA (3rd 1990 Bechyně, Czechoslovakia). Proceedings, DIANA III: Conference on discriminant analysis, cluster analysis, factor analysis, and related topics of multivariate statistics and data analysis : Bechyně, June 4-7, 1990. Praha: Mathematical Institute of the Czechoslovak Academy of Sciences, 1990.

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26

Tantri, Malini L. Performance of Indian SEZs: A disaggregated level analysis. Bangalore: Institute for Social and Economic Change, 2010.

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27

Institute for Social and Economic Change, ed. Performance of Indian SEZs: A disaggregated level analysis. Bangalore: Institute for Social and Economic Change, 2010.

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28

1960-, Decker Reinhold, and Lenz Hans-Joachim, eds. Advances in data analysis: Proceedings of the 30th Annual Conference of The Gesellschaft für Klassifikation e.V., Freie Universität Berlin, March 8-10, 2006. Berlin: Springer, 2007.

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29

Renski, Henry. Workforce Flows in the Maine Marine Trades Cluster: An analysis of the uses of matched establishment-employee databases in understanding labor market dynamics. Augusta, Maine: Center for Workforce Research and Information, Maine Department of Labor, 2009.

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30

L, Banks David, ed. Classification, clustering, and data mining applications: Proceedings of the Meeting of the International Federation of Classification Societies (IFCS), Illinois Institute of Technology, Chicago, 15-18 July 2004. Berlin: Springer, 2004.

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31

International Federation of Classification Societies. Conference. Advances in data science and classification: Proceedings of the 6th Conference of the International Federation of Classification Societies (IFCS-98), Università "La Sapienza", Rome, 21-24 July, 1998. New York: Springer, 1998.

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32

Cluster Analysis. Sage Publications (CA), 2012.

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33

Vogt, Wolfgang, D. Nagel, and H. Sator. Cluster Analysis in Clinical Chemistry: A Model. John Wiley & Sons, 1988.

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34

Cluster Randomized Trials (Interdisciplinary Statistics). Chapman & Hall/CRC, 2008.

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35

1950-, Morán-López J. L., Sanchez J. M, and International Workshop on Theory and Applications of the Cluster Variation and Path Probability Methods (1995 : San Juan Teotihuacán, Mexico), eds. Theory and applications of the cluster variation and path probability methods. New York: Plenum Press, 1996.

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36

Cluster and Classification Techniques for the Biosciences. Cambridge University Press, 2006.

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37

Fielding, Alan H. Cluster and Classification Techniques for the Biosciences. Cambridge University Press, 2007.

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38

Fielding, Alan H. Cluster and Classification Techniques for the Biosciences. Cambridge University Press, 2007.

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39

Bitter, Peter H. Von, and Glen K. Merrill. The Reconstruction of Fossil Organisms Using Cluster Analysis: A Case Study from Late Paleozoic Conodonts (Life Sciences Miscellaneous Publication). Royal Ontario Museum, 1990.

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40

E, Alexander F., and Boyle P, eds. Methods for investigating localized clustering of disease. Lyon, France: International Agency for Research on Cancer, World Health Organization, 1996.

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41

Methods for Investigating Localized Clustering of Disease (I a R C Scientific Publication). IARC Scientific Publications, 1997.

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42

(Editor), Andrew B. Lawson, and David G.T. Denison (Editor), eds. Spatial Cluster Modelling (Monographs on Statistics and Applied Probability). Chapman & Hall/CRC, 2002.

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43

Miksza, Peter, and Kenneth Elpus. Analyses of Data from Complex Survey Sampling. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0011.

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This chapter introduces the specialized techniques necessary for analyzing data that have been gathered in a complex or multistage survey sample. The chapter details the methods most commonly used to collect complex survey data and then explains the specific statistical tools that must be employed to correctly analyze complex survey data. First, an overview of the various types of sampling methods is presented, beginning with simple random sampling and moving through other methods to finally discuss the commonly employed research techniques of cluster sampling. The chapter continues with a discussion of survey weights—what they mean and how they are derived. The chapter concludes with software-based suggestions on the proper analysis of survey data.

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44

LP, StataCorp, ed. Stata multivariate statistics reference manual: Release 10. College Station, Tex: StataCorp LP, 2007.

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45

Gan, Guojun, Chaoqun Ma, and Jianhong Wu. Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability). SIAM, Society for Industrial and Applied Mathematics, 2007.

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46

Sutradhar, Brajendra C. Dynamic Mixed Models for Familial Longitudinal Data. Springer, 2013.

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47

Sutradhar, Brajendra C. Dynamic Mixed Models for Familial Longitudinal Data. Springer, 2011.

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48

Whitman, John, and Yohei Ono. Diachronic interpretations of word order parameter cohesion. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198747840.003.0004.

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This chapter uses statistical tools to investigate the interrelationship between typological features in the World Atlas of Language Structures Online (Dryer and Haspelmath 2013) in the WALS 201 language sample, with the objective of determining how crosscategorial word order generalizations might emerge as the result of syntactic change. Multiple Correspondence Analysis and a variety of cluster analyses show that word order features tend to group along the familiar lines of the Head Parameter. But there is an important caveat to this, previously noticed by Albu (2006): word order features in NP (e.g. [Order of noun and determiner], [Order of noun and adjective]) group separately from word order features in VP and PP, with the exception of [Order of noun and genitive]. We provide a diachronic explanation for this fact: nouns and their arguments may be reanalysed as PPs, or in the case of reanalysed nominalizations, clauses.

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49

Peacock, Janet L., and Phil J. Peacock. Oxford Handbook of Medical Statistics. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780198743583.001.0001.

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A good understanding of medical statistics is essential to evaluate medical research and to choose appropriate ways of implementing findings in clinical practice. The Oxford Handbook of Medical Statistics, second edition, has been written to provide doctors and medical students with a comprehensive yet concise account of this often difficult subject. Described by readers as a ‘statistical Bible’, this new edition maintains the accessibility and thoroughness of the original, and includes comprehensive updates including new sections on transitional medicine, cluster designs, and modern statistical packages. The handbook promotes understanding and interpretation of statistical methods across a wide range of topics, from study design and sample size considerations, through t and chi-squared tests, to complex multifactorial analyses, all using examples from published research. References and further reading are included, to allow deeper understanding on specific topics. Featuring a new chapter on how to use this book in different medical contexts, the Oxford Handbook of Medical Statistics helps readers to conduct their own research and critically appraise others' work.

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50

Stata multivariate statistics reference manual: Release 9. College Station, Tex: StataCorp LP, 2005.

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