Books on the topic 'Clustering based on correlation'

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1

Pedrycz, Witold. Knowledge-Based Clustering. New York: John Wiley & Sons, Ltd., 2005.

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2

Biehl, Michael, Barbara Hammer, Michel Verleysen, and Thomas Villmann, eds. Similarity-Based Clustering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01805-3.

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3

Pedrycz, Witold. Knowledge-Based Clustering. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471708607.

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4

Botbol, Joseph Moses. Multivariate clustering based on entropy. [Washington]: U.S. G.P.O., 1989.

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5

Botbol, Joseph Moses. Multivariate clustering based on entropy. Washington, DC: Dept. of the Interior, 1989.

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6

Jordan, James. Correlation-based measurement systems. Hemel Hempstead: Horwood, 1989.

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7

1949-, Bishop Peter, and Kiani Bijan, eds. Correlation-based measurement systems. Chichester: E. Horwood, 1989.

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8

Vathy-Fogarassy, Ágnes. Graph-Based Clustering and Data Visualization Algorithms. London: Springer London, 2013.

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9

Streekmann, Niels. Clustering-Based Support for Software Architecture Restructuring. Wiesbaden: Vieweg+Teubner Verlag, 2011. http://dx.doi.org/10.1007/978-3-8348-8675-0.

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10

Vathy-Fogarassy, Ágnes, and János Abonyi. Graph-Based Clustering and Data Visualization Algorithms. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5158-6.

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11

Arranz, Maria V. Sublanguage-based semantic clustering and disambiguation from corpora. Manchester: UMIST, 1997.

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12

David, Hutchison. Similarity-Based Clustering: Recent Developments and Biomedical Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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13

Riesen, Kaspar. Graph classification and clustering based on vector space embedding. New Jersey: World Scientific, 2010.

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14

Thrun, Michael Christoph. Projection-Based Clustering through Self-Organization and Swarm Intelligence. Wiesbaden: Springer Fachmedien Wiesbaden, 2018. http://dx.doi.org/10.1007/978-3-658-20540-9.

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15

Armitage, Emily G., Helen L. Kotze, and Kaye J. Williams. Correlation-based network analysis of cancer metabolism. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0615-4.

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16

Socolovsky, Eduardo A. A dissimilarity measure for clustering high- and infinite dimensional data that satisfies the triangle inequality. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 2002.

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17

Aguirre, L. A. Improved structure selection for nonlinear models based on term clustering. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1994.

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18

Billings, S. A. Correlation based model validity tests for nonlinear models. Sheffield: University, Dept. of Control Engineering, 1985.

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19

Schreiber, Thomas. A voronoi diagram based adaptive k-means-type clustering algorithm for multidimensional weighted data. Kaiserslautern: Universität Kaiserslautern, 1991.

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20

Rajan, P. K. Real-valued composite filters for correlation-based optical pattern recognition: Final report. [Washington, DC: National Aeronautics and Space Administration, 1992.

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21

Jackson, Amos R. Correlation of Muslim doctrine and Latter-day Saint doctrine: Based upon holy scriptures. Sandy, UT: Agreka Books, 1997.

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22

Armitage, Emily G. Correlation-based network analysis of cancer metabolism: A new systems biology approach in metabolomics. New York: Springer, 2014.

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23

Manzo, Ula C. Content area: A framework for reading-based instruction. 5th ed. Hoboken, NJ: J. Wiley & Sons, 2009.

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24

K, Kokula Krishna Hari, and K. Saravanan, eds. Identification of Brain Regions Related to Alzheimers’ Diseases using MRI Images Based on Eigenbrain and K-means Clustering. Tiruppur, Tamil Nadu, India: Association of Scientists, Developers and Faculties, 2016.

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25

Garibaldi, Luca. Trends in oceanic captures and clustering of large marine ecosystems: Two studies based on the FAO capture database. Rome: Food and Agriculture Organization of the United Nations, 2003.

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26

Fang, Zhihui. Language and literacy in inquiry-based science classrooms, grades 3-8. Thousand Oaks, Calif: Corwin Press, 2010.

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27

Brinton, Donna. Content-based second language instruction. Boston, Mass: Heinle & Heinle Publishers, 1989.

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28

Thrun, Michael Christoph. Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data. Cham: Springer Nature, 2018.

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29

Brinton, Donna. Content-based second language instruction. Ann Arbor: University of Michigan Press, 2004.

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30

Brinton, Donna. Content-based second language instruction. New York: Newbury House Publishers, 1989.

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31

Y, Austin Theresa, ed. Content-based second language teaching and learning: An interactive approach. Boston: Pearson/Allyn and Bacon, 2004.

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32

Excise, Great Britain Customs and. HM Customs & Excise tariff correlation tables: Correlating the 1987 CCCN based tariff codes withthe 1988 harmonised system based tariff codes and including guidance notes for reclassification. London: HMSO, 1987.

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33

M, Linek Wayne, ed. Content literacy: An inquiry-based case approach. Upper Saddle River, NJ: Pearson/Merrill Prentice Hall, 2004.

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34

Leonard, Lamme Linda, and Pringle Rose M, eds. Language and literacy in inquiry-based science classrooms, grades 3-8. Thousand Oaks, Calif: Corwin Press, 2010.

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35

Ann, Snow Marguerite, and Brinton Donna, eds. The content-based classroom: Perspectives on integrating language and content. White Plains, N.Y: Longman, 1997.

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36

Y, Austin Theresa, ed. Content-based second language teaching and learning: An interactive approach. Boston: Pearson, 2004.

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37

1943-, Vilardi Teresa, and Chang Mary 1972-, eds. Writing-based teaching: Essential practices and enduring questions. Albany: State University of New York, 2009.

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38

1969-, Schillinger Trace, and Stock Andrew 1969-, eds. Entering the conversations: Practicing literacy in the disciplines. Urbana, Illinois: NCTE, National Council of Teachers of English, 2014.

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39

Carol, Rothenberg, and Frey Nancy 1959-, eds. Content-area conversations: How to plan discussion-based lessons for diverse language learners. Alexandria, VA: Association for Supervision and Curriculum Development, 2008.

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40

Reynolds, Kate Mastruserio. Approaches to inclusive English classrooms: A teacher's handbook for content based instruction. Bristol: Multilingual Matters, 2015.

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41

Los, Sietse Oene. Linkages between global vegetation and climate: An analysis based on NOAA advanced very high resolution radiometer data : contract NAS-31752. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1998.

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42

GarcÍa-Soriano, David, Francesco Gullo, and Francesco Bonchi. Correlation Clustering. Morgan & Claypool Publishers, 2022.

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43

GarcÍa-Soriano, David, Francesco Gullo, and Francesco Bonchi. Correlation Clustering. Morgan & Claypool Publishers, 2022.

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44

Francesco, Bonchi, García-Soriano David, and Gullo Francesco. Correlation Clustering. Springer International Publishing AG, 2022.

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45

García-Soriano, David, Francesco Gullo, and Francesco Bonchi. Correlation Clustering: Morgan and Claypool Publishers. Morgan & Claypool Publishers, 2022.

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46

B. Hammer,Thomas Villmann,M. Biehl. Similarity-Based Clustering. Springer, 2009.

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47

Abonyi, János, and Ágnes Vathy-Fogarassy. Graph-Based Clustering and Data Visualization Algorithms. Springer, 2013.

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48

Abonyi, János, and Ágnes Vathy-Fogarassy. Graph-Based Clustering and Data Visualization Algorithms. Springer, 2013.

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49

Bonnay, Denis. A Clustering-Based Approach to Collective Beliefs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190680534.003.0008.

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In this chapter, I argue in favor of a new approach to collective beliefs in unorganized groups, in terms of doxastic clustering. When a group does not have dedicated mechanisms for production of collective beliefs, and when individual beliefs of members of the group are diverse, it does not make much sense to attribute to the group some average beliefs or any other kind of collective beliefs produced by aggregating individual beliefs. Rather, beliefs are meaningfully attributed to coherent subgroups of individuals who share similar opinions. In this case, attribution of collective beliefs involves both clustering, that is partitioning the group into coherent doxastic units, and aggregation, that is aggregating individual opinions within coherent clusters. Adapting standard judgment aggregation theory, I propose a formal framework for doxastic clustering and provide an axiomatic characterization of majoritarian intra-cluster aggregation.
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50

Streekmann, Niels. Clustering-Based Support for Software Architecture Restructuring. Vieweg Verlag, Friedr, & Sohn Verlagsgesellschaft mbH, 2011.

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