Unsupervised machine learning for clustering in political and social research

著者

    • Waggoner, Philip D.

書誌事項

Unsupervised machine learning for clustering in political and social research

Philip D. Waggoner

（Cambridge elements, . Elements in quantitative and computational methods for the social sciences / edited by R. Michael Alvarez, Nathaniel Beck）

Cambridge University Press, 2020

  • : pbk

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注記

Includes bibliographical references (p. [58]-60)

内容説明・目次

内容説明

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.

目次

  • 1. Introduction
  • 2. Setting the stage for clustering
  • 3. Agglomerative hierarchical clustering
  • 4. k-means clustering
  • 5. Gaussian mixture models
  • 6. Advanced methods
  • 7. Conclusion.

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