Unsupervised Machine Learning for Clustering in Political and Social - Researc

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.

List of contents

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.

Summary

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.

Product details

• Authors: Philip D Waggoner, Philip D. Waggoner, Philip D. (University of Chicago) Waggoner
• Publisher: Cambridge University Press ELT
• Languages: English
• Product format: Paperback / Softback
• Released: 31.10.2020
• EAN: 9781108793384
• ISBN: 978-1-108-79338-4
• No. of pages: 75
• Series: Elements in Quantitative and C; Elements in Quantitative and Computational Methods for the Social Sciences
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > General, dictionaries; Social sciences, law, business > Media, communication > Communication science; Data Mining, POLITICAL SCIENCE / General, Social research & statistics, Coding theory & cryptology, Social research and statistics, Database design and theory, Research methods: general, Coding theory and cryptology, Data capture and analysis, Data Capture & Analysis, Database design & theory