Unsupervised Machine Learning for Clustering in Political and Social Research (Paperback)

Philip D. Waggoner

ISBN 10: 110879338X / ISBN 13: 9781108793384

Editore: Cambridge University Press, Cambridge, 2021

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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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Codice articolo 9781108793384

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Titolo: Unsupervised Machine Learning for Clustering...

Casa editrice: Cambridge University Press, Cambridge

Data di pubblicazione: 2021

Legatura: Paperback

Condizione: new

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Riassunto:

Offers researchers and teachers an introduction to clustering, with R code and real data to facilitate interaction with the concepts.