Description
| Product ID: | 9781108793384 |
| Product Form: | Paperback / softback |
| Country of Manufacture: | GB |
| Series: | Elements in Quantitative and Computational Methods for the Social Sciences |
| Title: | Unsupervised Machine Learning for Clustering in Political and Social Research |
| Authors: | Author: Philip D. Waggoner |
| Page Count: | 75 |
| Subjects: | Coding theory and cryptology, Coding theory & cryptology, Research methods: general, Social research and statistics, Database design and theory, Data capture and analysis, Data mining, Research methods: general, Social research & statistics, Database design & theory, Data capture & analysis, Data mining |
| Description: | Select Guide Rating 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. |
| Imprint Name: | Cambridge University Press |
| Publisher Name: | Cambridge University Press |
| Country of Publication: | GB |
| Publishing Date: | 2021-01-28 |
