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Готові списки джерел за темами / Module clustering / Книги

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Module clustering.

Книги з теми "Module clustering"

Автор: Grafiati

Опубліковано: 17 січня 2024

Оновлено: 31 липня 2025

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Ознайомтеся з топ-42 книг для дослідження на тему "Module clustering".

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1

J, Davis Cecil, Herman Irving P, and Turner Terry R, eds. Process module metrology, control, and clustering, 11-13 September 1991, San Jose, Calif. SPIE--International Society for Optical Engineering, 1991.

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2

J, Davis Cecil, Herman Irving P, Turner Terry R, and Society of Photo-Optical Instrumentation Engineers., eds. Process module metrology, control, and clustering: 11-13 September 1991, San Jose, California. SPIE, 1992.

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3

Sergiy, Butenko, Chaovalitwongse W. Art, Pardalos P. M. 1954-, and DIMACS Workshop on Clustering Problems in Biological Networks (2006 : Rutgers University), eds. Clustering challenges in biological networks. World Scientific, 2009.

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4

Wilson, Caroline L. Clustering algorithms and mathematical modeling. Nova Science Publishers, 2010.

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5

Rachev, S. T. Financial models with Lévy processes and volatility clustering. John Wiley, 2011.

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6

Sergiy, Butenko, Chaovalitwongse W. Art, Pardalos P. M. 1954-, and DIMACS Workshop on Clustering Problems in Biological Networks (2006 : Rutgers University), eds. Clustering challenges in biological networks. World Scientific, 2009.

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7

Tsangarides, Charalambos G. What is fuzzy about clustering in West Africa? International Monetary Fund, African Dept., 2006.

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8

Grigor'ev, Anatoliy, and Evgeniy Isaev. Methods and algorithms of data processing. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1032305.

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The tutorial deals with selected methods and algorithms of data processing, the sequence of solving problems of processing and analysis of data to create models behavior of the object taking into account all the components of its mathematical model. Describes the types of technological methods for the use of software and hardware for solving problems in this area. The algorithms of distributions, regressions vremenny series, transform them with the aim of obtaining mathematical models and prediction of the behavior information and economic systems (objects). The second edition is supplem

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9

E, MacCuish Norah, ed. Clustering in bioinformatics and drug discovery. Taylor & Francis, 2011.

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10

Thurow, Amy Purvis. Toward an augmented theory of cooperative behavior: The case of clustering in animal agriculture. Dept. of Agricultural Economics, Texas A & M University, 1998.

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11

Kaufmann, Sylvia. The business cycle of European countries: Bayesian clustering of country-individual IP growth series. Oesterreichische Nationalbank, 2003.

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12

Sanghamitra, Bandyopadhyay, Mukhopadhyay Anirban, and SpringerLink (Online service), eds. Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics. Springer-Verlag Berlin Heidelberg, 2011.

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13

Bessonov, Aleksey. The study of criminal activity using artificial intelligence. INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2195488.

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The monograph describes the technology of building digital crime models, including the preparation of data on criminal acts for study using mathematical statistics and artificial intelligence methods, the features of studying such data through various artificial intelligence methods, including neural networks, gradient boosting, decision trees, random forest, clustering, etc. Special attention is paid to the use of mathematical statistics and artificial intelligence methods in the study of serial crimes in science and practice. It is intended for scientists and practitioners of law enforcement

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14

Butenko, Sergiy. Clustering Challenges in Biological Networks. World Scientific Publishing Co Pte Ltd, 2009.

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15

MacCuish, John David, and Norah E. MacCuish. Clustering in Bioinformatics and Drug Discovery. Taylor & Francis Group, 2010.

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16

MacCuish, Norah E., and Maccuish John David. Clustering in Bioinformatics and Drug Discovery. Taylor & Francis Group, 2010.

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17

MacCuish, John David, and Norah E. MacCuish. Clustering in Bioinformatics and Drug Discovery. Taylor & Francis Group, 2010.

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18

J, Fabozzi CFA Frank, Svetlozar T. Rachev, Michele L. Bianchi, and Young Shin Kim. Financial Models with Levy Processes and Volatility Clustering. Wiley & Sons, Incorporated, John, 2011.

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19

Fabozzi, Frank J., Svetlozar T. Rachev, Michele L. Bianchi, and Young Shin Kim. Financial Models with Levy Processes and Volatility Clustering. Wiley & Sons, Incorporated, John, 2011.

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20

Fabozzi, Frank J., Svetlozar T. Rachev, Michele L. Bianchi, and Young Shin Kim. Financial Models with Levy Processes and Volatility Clustering. Wiley & Sons, Incorporated, John, 2011.

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21

Fabozzi, Frank J., Svetlozar T. Rachev, Michele L. Bianchi, and Young Shin Kim. Financial Models with Levy Processes and Volatility Clustering. Wiley & Sons, Limited, John, 2011.

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22

Fabozzi, Frank J., Svetlozar T. Rachev, Michele L. Bianchi, and Young Shin Kim. Financial Models with Levy Processes and Volatility Clustering. Wiley & Sons, Incorporated, John, 2011.

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23

Newman, Mark. The configuration model. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0012.

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A discussion of the most fundamental of network models, the configuration model, which is a random graph model of a network with a specified degree sequence. Following a definition of the model a number of basic properties are derived, including the probability of an edge, the expected number of multiedges, the excess degree distribution, the friendship paradox, and the clustering coefficient. This is followed by derivations of some more advanced properties including the condition for the existence of a giant component, the size of the giant component, the average size of a small component, an

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24

Grogan-Kaylor, Andrew. Multilevel Thinking. Oxford University PressNew York, NY, 2025. https://doi.org/10.1093/9780197806494.001.0001.

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Abstract This book introduces the ideas of multilevel modeling. Chapters cover: the cross-sectional model; the longitudinal model; models for binary outcomes; and models for more complicated structures with multiple levels, or levels that are cross classified. Throughout the book, there is a focus on pedagogical effectiveness and intuitive presentation. Interwoven throughout the book also is the idea that multilevel models do not only allow one to control for statistical clustering or nesting but also allow one to explore the variation and diversity across a data set. This book provides a tuto

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25

Introduction to Computational Neurobiology and Clustering. World Scientific Publishing Co Pte Ltd, 2007.

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26

Introduction to Computational Neurobiology and Clustering. World Scientific Publishing Co Pte Ltd, 2007.

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27

Bolshoy, Alexander, Zeev Volkovich, and Valery Kirzhner. Genome Clustering: From Linguistic Models to Classification of Genetic Texts. Springer, 2010.

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28

Bolshoy, Alexander, Zeev Volkovich, Valery Kirzhner, and Zeev Barzily. Genome Clustering: From Linguistic Models to Classification of Genetic Texts. Springer, 2012.

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29

Bandyopadhyay, Sanghamitra, Ujjwal Maulik, and Anirban Mukhopadhyay. Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics. Springer, 2011.

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30

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Specific constructions. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0009.

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This chapter presents network-generating models which cannot be neatly categorized as growing, nor as defined primarily through a target degree distribution. They are best understood as mechanistic constructions designed to elucidate a particular feature of the network. In the first sub-section, the Watts–Strogatz model is introduced and motivated as a construction to achieve both a high degree of clustering and a low average path length. Geometric graphs, in their Euclidian flavour, are shown to be a natural choice for broadcast networks. The Hyperbolic variant is informally described, becaus

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31

Multiobjective Genetic Algorithms for Clustering: Applications in Data Mining and Bioinformatics. Springer Berlin / Heidelberg, 2014.

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32

Newman, Mark. Random graphs. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0011.

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An introduction to the mathematics of the Poisson random graph, the simplest model of a random network. The chapter starts with a definition of the model, followed by derivations of basic properties like the mean degree, degree distribution, and clustering coefficient. This is followed with a detailed derivation of the large-scale structural properties of random graphs, including the position of the phase transition at which a giant component appears, the size of the giant component, the average size of the small components, and the expected diameter of the network. The chapter ends with a dis

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33

Bentham, James R. The genetics of congenital heart disease. Edited by José Maria Pérez-Pomares, Robert G. Kelly, Maurice van den Hoff, et al. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198757269.003.0022.

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Congenital heart disease (CHD) is defined as a structural cardiac malformation resulting from an abnormality of development; 8% of CHD is inherited in a Mendelian fashion and 12% results from chromosomal imbalance. Recurrence risk and new research suggest that even the remaining 80% of patients without an identifiable familial or syndromic basis for disease may have an identifiable genetic cause. The potential to understand these mechanisms is increasing with the advent of new sequencing techniques which have identified multiple or single rare variants and/or copy number variants clustering in

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34

Gebreab, Samson Y. Statistical Methods in Spatial Epidemiology. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190843496.003.0004.

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Most studies evaluating relationships between neighborhood characteristics and health neglect to examine and account for the spatial dependency across neighborhoods, that is, how neighboring areas are related to each other, although the possible presence of spatial effects (e.g., spatial dependency, spatial heterogeneity) can potentially influence the results in substantial ways. This chapter first discusses the concept of spatial autocorrelation and then provides an overview of different spatial clustering methods, including Moran’s I and spatial scan statistics as well as different models to

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35

Rajakumar, P. S., S. Geetha, and T. V. Ananthan. Fundamentals of Image Processing. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-80-8.

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"Fundamentals of Image Processing" offers a comprehensive exploration of image processing's pivotal techniques, tools, and applications. Beginning with an overview, the book systematically categorizes and explains the multifaceted steps and methodologies inherent to the digital processing of images. The text progresses from basic concepts like sampling and quantization to advanced techniques such as image restoration and feature extraction. Special emphasis is given to algorithms and models crucial to image enhancement, restoration, segmentation, and application. In the initial segments, the i

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36

Newman, Mark. Community structure. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0014.

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A discussion of community structure in networks and methods for its detection. The chapter begins with an introduction to the idea of community structure, followed by descriptions of a range of methods for finding communities, including modularity maximization, the InfoMap method, methods based on maximum-likelihood fits of models to network data, betweenness-based methods, and hierarchical clustering. Also discussed are methods for assessing algorithm performance, along with a summary of performance studies and their findings. The chapter concludes with a discussion of other types of large-sc

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37

Zhiming, Bao. The Systemic Nature of Substratum Transfer. Edited by Markku Filppula, Juhani Klemola, and Devyani Sharma. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199777716.013.024.

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This chapter discusses four grammatical systems in Singapore English that are transferred from Chinese: aspect, pragmatic particles, topicalization, and quantification. Proper analysis of the relevant substrate features reveals extensive clustering: features which form a grammatical system transfer together. Substratum transfer targets the grammatical system, and the transferred system is then exponenced with suitable morphosyntactic materials from the lexifier, filtering out those component features for which the lexifier has no well-formed morphosyntactic exponent. The analysis of the four s

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38

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Applications of random graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0011.

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This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case stu

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39

James, Gareth. Sparseness and functional data analysis. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.11.

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This article considers two functional data analysis settings where sparsity becomes important: the first involves only measurements at a relatively sparse set of points and the second relates to variable selection in a functional case. It begins with a discussion of two data sets that fall into the ‘sparsely observed’ category, the ‘growth’ data and the ‘nephropathy’ data, both of which are used to illustrate alternative approaches for analysing sparse functional data. It then examines different classes of methods that can be applied to functional data, such as basis functions, mixed-effects m

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40

Wang, Bin. Intraseasonal Modulation of the Indian Summer Monsoon. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.616.

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The strongest Indian summer monsoon (ISM) on the planet features prolonged clustered spells of wet and dry conditions often lasting for two to three weeks, known as active and break monsoons. The active and break monsoons are attributed to a quasi-periodic intraseasonal oscillation (ISO), which is an extremely important form of the ISM variability bridging weather and climate variation. The ISO over India is part of the ISO in global tropics. The latter is one of the most important meteorological phenomena discovered during the 20th century (Madden & Julian, 1971, 1972). The extreme dry an

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41

Lincoln, James R., and Matthew Sargent. Business Groups as Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198717973.003.0004.

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This chapter explores how business groups can be viewed as networks; whether and how some groups are more “network-like” than others; and how formal network concepts and analytic methods may facilitate the study of a number of salient problems in business-group research. Much of the business-group literature treats a firm’s affiliation with a group as an “all or nothing” dichotomy. The network lens, however, forces the analyst to unpack the coarse dichotomy of “group” and “stand-alone” into an array of constituent relations, equivalences, and complementarities, which can in turn be mapped to o

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42

Introduction to Computational Neurobiology and Clustering (Series on Advances in Mathematics for Applied Sciences) (Series on Advances in Mathematics for Applied Sciences). World Scientific Publishing Company, 2007.

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