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ISBN 10: 110879338X ISBN 13: 9781108793384
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ISBN 10: 110879338X ISBN 13: 9781108793384
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Paperback or Softback. Condizione: New. Unsupervised Machine Learning for Clustering in Political and Social Research. Book.
Lingua: Inglese
Editore: Cambridge University Press, 2021
ISBN 10: 110879338X ISBN 13: 9781108793384
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ISBN 10: 110879338X ISBN 13: 9781108793384
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Paperback. Condizione: new. Paperback. In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering. Offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered, in addition to R code and real data to facilitate interaction with the concepts. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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ISBN 10: 110879338X ISBN 13: 9781108793384
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Aggiungi al carrelloKartoniert / Broschiert. Condizione: New. Offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered, in addi.
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ISBN 10: 110879338X ISBN 13: 9781108793384
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware - Offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered, in addition to R code and real data to facilitate interaction with the concepts.
Lingua: Inglese
Editore: Cambridge University Press, 2021
ISBN 10: 110879338X ISBN 13: 9781108793384
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Unsupervised Machine Learning for Clustering in Political and Social Research | Philip D Waggoner | Taschenbuch | Kartoniert / Broschiert | Englisch | 2021 | Cambridge University Press | EAN 9781108793384 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Lingua: Inglese
Editore: Cambridge University Press, 2021
ISBN 10: 110879338X ISBN 13: 9781108793384
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Lingua: Inglese
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ISBN 10: 110879338X ISBN 13: 9781108793384
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering. Offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered, in addition to R code and real data to facilitate interaction with the concepts. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Lingua: Inglese
Editore: Cambridge University Press, Cambridge, 2021
ISBN 10: 110879338X ISBN 13: 9781108793384
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering. Offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered, in addition to R code and real data to facilitate interaction with the concepts. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.