Unsupervised Machine Learning for Clustering in Political and Social Research (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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Series:	Elements in Quantitative and Computational Methods for the Social Sciences
Release date:	2021
Authors:	Philip D. Waggoner
Dimensions:	150 x 230 x 5mm (L x W x T)
Format:	Paperback - Trade
Pages:	75
ISBN-13:	978-1-108-79338-4
Categories:	Books > Reference & Interdisciplinary > Communication studies > Coding theory & cryptology Books > Reference & Interdisciplinary > Communication studies > Research methods Books > Social sciences > Sociology, social studies > Social research & statistics > General Books > Computing & IT > Applications of computing > Databases > Data capture & analysis Books > Computing & IT > Applications of computing > Databases > Data mining Promotions
LSN:	1-108-79338-X
Barcode:	9781108793384

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