Unsupervised Machine Learning for Clustering in Political and Social Research (Elements in Quantitative and Computational Methods for the Social Sciences) ダウンロード

Isbn 10: 110879338X

Isbn 13: 978-1108793384

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本の説明

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.

著者 :Philip D. Waggoner
Isbn 10 :110879338X
Isbn 13 :978-1108793384
によって公開 :2021/1/28
ページ数 :75ページ
出版社 :Cambridge University Press
言語 Unsupervised Machine Learning for Clustering in Political and Social Research (Elements in Quantitative and Computational Methods for the Social Sciences):英語
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