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Books on the topic 'Hierarchical cluster'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Dandamudi, Sivarama. Hierarchical Scheduling in Parallel and Cluster Systems. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0133-6.

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2

Dandamudi, Sivarama. Hierarchical Scheduling in Parallel and Cluster Systems. Springer US, 2003.

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3

Padmore, Joanne. An information based measure of dissimilarity for hierarchical cluster analysis. Sheffield University Management School, 1993.

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4

Nixon, J. How does the UK NHS compare with European standards?: A review of EU health care systems using hierarchical cluster analysis. Centre for Health Economics, University of York, 2000.

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5

Dandamudi, Sivarama. Hierarchical Scheduling in Parallel and Cluster Systems. Springer, 2003.

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6

Hierarchical scheduling in parallel and cluster systems. Kluwer Academic/Plenum Publishers, 2003.

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7

Dandamudi, Sivarama. Hierarchical Scheduling in Parallel and Cluster Systems. Springer, 2012.

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8

(Editor), Andreas Dress, and A. Von Haeseler (Editor), eds. Trees and Hierarchical Structures: Proceedings of Conference Held at Bielefeld, Frg, Oct. 1987 (Lecture Notes in Biomathematics). Springer, 1990.

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9

Zang, Shuang-Quan. Atom-Precise Metal Clusters: Surface Engineering and Hierarchical Assembly. Wiley & Sons, Incorporated, John, 2024.

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10

Zang, Shuang-Quan. Atom-Precise Metal Clusters: Surface Engineering and Hierarchical Assembly. Wiley & Sons, Incorporated, John, 2024.

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11

Zang, Shuang-Quan. Atom-Precise Metal Clusters: Surface Engineering and Hierarchical Assembly. Wiley & Sons, Limited, John, 2024.

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12

Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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13

McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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14

McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2020.

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McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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McNicholas, Paul D. Mixture Model-Based Classification. Taylor & Francis Group, 2016.

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18

Sklar, Marisa, Joanna C. Moullin, and Gregory A. Aarons. Study Design, Data Collection, and Analysis in Implementation Science. Edited by David A. Chambers, Wynne E. Norton, and Cynthia A. Vinson. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190647421.003.0006.

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This chapter provides an introduction to study design, data collection, and analysis in implementation science. Although the randomized controlled trial is frequently employed in implementation science, a number of alternatives are relied on for addressing the unique challenges present. Alternatives include the cluster randomized control trial, roll-out designs such as the stepped wedge, cumulative trial, and effectiveness–implementation hybrid designs. Data collection and data analytic techniques must also address the unique challenges present in implementation science. Often, implementation

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19

Lund, Martin. Whiteness. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/13503.001.0001.

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The socially constructed phenomenon of whiteness: how it was created, how it changes, and how it protects and privileges people who are perceived as white. This volume in the MIT Press Essential Knowledge series examines the socially constructed phenomenon of whiteness, tracing its creation, its changing formation, and its power to privilege and protect people who are perceived as white. Whiteness, author Martin Lund explains, is not one single idea but a shifting, overarching category, a flexible cluster of historically, culturally, and geographically contingent ideals and standards that enab

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20

Miksza, Peter, and Kenneth Elpus. Multilevel Models. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0012.

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This chapter introduces a statistical approach for analyzing nested data structures that both accounts for the dependence of observations due to hierarchical arrangements and allows for testing hypotheses at multiple levels. The most common application of multilevel models is for analyses of objects (e.g., people) nested within groups or clusters of some sort. Multilevel models can also be applied to longitudinal data analyses such that the “levels” do not refer to objects nested within groups but instead refer to multiple measurements (e.g., measures made at different occasions/time points) n

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