Unsupervised Machine Learning for Clustering in Political and Social Research

Philip D. Waggoner (University of Chicago)

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English

Cambridge University Press
28 January 2021

Research methods: general; Social research & statistics; Database design & theory; Data capture & analysis; Data mining

Series: Elements in Quantitative and Computational Methods for the Social Sciences

Summary
Details

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.

By: Philip D. Waggoner (University of Chicago)
Imprint: Cambridge University Press
Country of Publication: United Kingdom
Dimensions: Height: 150mm, Width: 230mm, Spine: 5mm
Weight: 140g
ISBN: 9781108793384
ISBN 10: 110879338X
Series: Elements in Quantitative and Computational Methods for the Social Sciences
Pages: 75
Publication Date: 28 January 2021
Audience: General/trade , ELT Advanced
Format: Paperback
Publisher's Status: Active