eBook - PDF

Unsupervised Machine Learning for Clustering in Political and Social Research

Name: Unsupervised Machine Learning for Clustering in Political and Social Research
ISBN: 9781108879835

Philip D. Waggoner,

English
PDF
Available on iOS & Android

eBook - PDF

Unsupervised Machine Learning for Clustering in Political and Social Research

Philip D. Waggoner,

About this book

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.

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Yes, you can access Unsupervised Machine Learning for Clustering in Political and Social Research by Philip D. Waggoner in PDF and/or ePUB format, as well as other popular books in Politics & International Relations & Politics. We have over one million books available in our catalogue for you to explore.