Unsupervised Machine Learning for Clustering in Political and Social Research (Elements in Quantitative and Computational Methods for the Social Sciences)

Waggoner, Philip D.

ISBN 10: 110879338X / ISBN 13: 9781108793384

Published by Cambridge University Press, 2021

About this Item

Seller Inventory # ABLIING23Mar2317530287611

Bibliographic Details

• Title: Unsupervised Machine Learning for Clustering in Political and Social Research (Elements in Quantitative and Computational Methods for the Social Sciences)
• Publisher: Cambridge University Press
• Publication Date: 2021
• Language: English
• ISBN 10: 110879338X
• ISBN 13: 9781108793384
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.