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Books on the topic 'Module clustering'

Author: Grafiati

Published: 17 January 2024

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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. Bellingham, Wash: 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. Bellingham, Wash: SPIE, 1992.

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3

Financial models with Levy processes and volatility clustering. Hoboken, N.J: Wiley, 2011.

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4

Wilson, Caroline L. Clustering algorithms and mathematical modeling. Hauppauge, N.Y: Nova Science Publishers, 2010.

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5

Rachev, S. T. Financial models with Lévy processes and volatility clustering. Hoboken, N.J: 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. New Jersry: World Scientific, 2009.

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7

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. New Jersry: World Scientific, 2009.

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8

Tsangarides, Charalambos G. What is fuzzy about clustering in West Africa? [Washington, D.C.]: International Monetary Fund, African Dept., 2006.

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9

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

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10

Grigor'ev, Anatoliy, and Evgeniy Isaev. Methods and algorithms of data processing. ru: 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 supplemented by materials that are in demand by researchers in the part of the correct use of clustering algorithms. Are elements of the classification algorithms to identify their capabilities, strengths and weaknesses. Are the procedures of justification and verify the adequacy of the results of the cluster analysis, conducted a comparison and evaluation of different clustering techniques, given information about visualization of multidimensional data and examples of practical application of clustering algorithms. Meets the requirements of Federal state educational standards of higher education of the last generation. For students of economic specialties, specialists, and graduate students.

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11

Genome clustering: From linguistic models to classification of genetic texts. Berlin: Springer, 2010.

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12

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

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13

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

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14

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

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15

The Art of Clustering - German Edition [PREVIEW]: Die Kunst des Clusterns. Wiesbaden, Germany: Springer VS, 2013.

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16

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

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17

Clustering in Bioinformatics and Drug Discovery. Taylor & Francis Group, 2019.

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18

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

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19

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

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20

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

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21

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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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

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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24

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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25

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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26

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, and the expected diameter. Generating function methods for network models are also introduced and used to perform some more advanced calculations, such as the calculation of the distribution of the number of second neighbors of a node and the complete distribution of sizes of small components. The chapter ends with a brief discussion of extensions of the configuration model to directed networks, bipartite networks, networks with degree correlations, networks with high clustering, and networks with community structure, among other possibilities.

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27

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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28

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

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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, because it is known to be a natural space in which to embed hierarchical graphs. Planar graphs have very specific real-world applications, but are extraordinarily challenging to analyze mathematically. Finally, weighted graphs allow for concepts such as traffic to be incorporated into the random graph model.

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31

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 discussion of some of the shortcomings of the random graph model.

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32

Bentham, James R. The genetics of congenital heart disease. Edited by José Maria Pérez-Pomares, Robert G. Kelly, Maurice van den Hoff, José Luis de la Pompa, David Sedmera, Cristina Basso, and Deborah Henderson. 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 cardiac developmental genes as well as common variants that may also contribute to disease, for example by altering metabolic pathways. Work in model organisms such as mouse and zebrafish has been pivotal in identifying CHD candidate genes. Future challenges involve translating the discoveries made in mouse models to human CHD genetics and manipulating potentially protective pathways to prevent disease.

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33

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 map spatial data, for example, spatial Bayesian mapping. Next, this chapter discusses various spatial regression methods used in spatial epidemiology for accounting spatial dependency and/or spatial heterogeneity in modeling the relationships between neighborhood characteristics and health outcomes, including spatial econometric models, Bayesian spatial models, and multilevel spatial models.

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34

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 intricacies of digital imaging systems, pixel connectivity, color models, and file formats are dissected. Following this, image enhancement techniques, including spatial and frequency domain methods and histogram processing, are elaborated upon. The book then addresses image restoration, discussing degradation models, noise modeling, and blur, and offers insights into the compelling world of multi-resolution analysis with in-depth discussions on wavelets and image pyramids. Segmentation processes, especially edge operators, boundary detections, and thresholding techniques, are detailed in subsequent chapters. The text culminates by diving deep into the applications of image processing, exploring supervised and unsupervised learning, clustering algorithms, and various classifiers. Throughout the discourse, practical examples, real-world applications, and intuitive diagrams are integrated to facilitate an enriched learning experience. This book stands as an essential guide for both novices aiming to grasp the basics and experts looking to hone their knowledge in image processing. Keywords: Digital Imaging Systems, Image Enhancement, Image Restoration, Multi-resolution Analysis, Wavelets, Image Segmentation, Feature Extraction, SIFT, SURF, Image Classifiers, Supervised Learning, Clustering Algorithms.

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35

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-scale structure in networks, such as overlapping and hierarchical communities, core-periphery structure, latent-space structure, and rank structure.

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36

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 systems shows that post-transfer stabilization is subject to the normative effect of English. It is argued that data obtained through introspection and corpora are complementary, and substrate-induced grammatical change is best accounted for in a usage-based model that uses the two types of data.

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37

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 study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.

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38

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 models and local smoothing techniques, as well as specific methodologies for dealing with sparse functional data in the principal components, clustering, classification, and regression settings. Finally, it describes two approaches for performing regressions involving a functional predictor and a scalar response: SASDA (sequential algorithm for selecting design automatically) and FLiRTI (Functional Linear Regression That’s Interpretable).

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39

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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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 and wet events are regulated by the boreal summer ISO (BSISO). The BSISO over Indian monsoon region consists of northward propagating 30–60 day and westward propagating 10–20 day modes. The “clustering” of synoptic activity was separately modulated by both the 30–60 day and 10–20 day BSISO modes in approximately equal amounts. The clustering is particularly strong when the enhancement effect from both modes acts in concert. The northward propagation of BSISO is primarily originated from the easterly vertical shear (increasing easterly winds with height) of the monsoon flows, which by interacting with the BSISO convective system can generate boundary layer convergence to the north of the convective system that promotes its northward movement. The BSISO-ocean interaction through wind-evaporation feedback and cloud-radiation feedback can also contribute to the northward propagation of BSISO from the equator. The 10–20 day oscillation is primarily produced by convectively coupled Rossby waves modified by the monsoon mean flows. Using coupled general circulation models (GCMs) for ISO prediction is an important advance in subseasonal forecasts. The major modes of ISO over Indian monsoon region are potentially predictable up to 40–45 days as estimated by multiple GCM ensemble hindcast experiments. The current dynamical models’ prediction skills for the large initial amplitude cases are approximately 20–25 days, but the prediction of developing BSISO disturbance is much more difficult than the prediction of the mature BSISO disturbances. This article provides a synthesis of our current knowledge on the observed spatial and temporal structure of the ISO over India and the important physical processes through which the BSISO regulates the ISM active-break cycles and severe weather events. Our present capability and shortcomings in simulating and predicting the monsoon ISO and outstanding issues are also discussed.

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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 outcomes such as strategy, operations, and performance. We first consider how attention to such formal network properties as density, connectivity, centrality, and clustering may advance business-group research. We then examine the degree to which a number of group configurations approximate the ideal type “network form”—a leading-edge mode of economic organization in the global economy.

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