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Elements in Quantitative and Computational Methods for the Social Sciences Ser.: Unsupervised Machine Learning for Clustering in Political and Social Research by Philip D. Waggoner (2021, Trade Paperback)
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A suite of widely used clustering techniques is covered, in addition to R code and real data to facilitate interaction with the concepts. Publisher Cambridge University Press. Format Paperback. Author Philip D. Waggoner.
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About this product
Product Identifiers
PublisherCambridge University Press
ISBN-10110879338X
ISBN-139781108793384
eBay Product ID (ePID)16050084104
Product Key Features
Number of Pages75 Pages
Publication NameUnsupervised Machine Learning for Clustering in Political and Social Research
LanguageEnglish
Publication Year2021
SubjectGeneral
TypeTextbook
Subject AreaPolitical Science, Social Science
AuthorPhilip D. Waggoner
SeriesElements in Quantitative and Computational Methods for the Social Sciences Ser.
FormatTrade Paperback
Dimensions
Item Height0.2 in
Item Weight4.9 Oz
Item Length5.9 in
Item Width9.1 in
Additional Product Features
Intended AudienceScholarly & Professional
Dewey Edition23
IllustratedYes
Dewey Decimal300.72
Table Of Content1. Introduction; 2. Setting the stage for clustering; 3. Agglomerative hierarchical clustering; 4. k-means clustering; 5. Gaussian mixture models; 6. Advanced methods; 7. Conclusion.
SynopsisOffers researchers and teachers an introduction to clustering, with R code and real data to facilitate interaction with the concepts., 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 addition to R code and real data to facilitate interaction with the concepts., 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.
LC Classification NumberH62
