Share
Unsupervised Machine Learning for Clustering in Political and Social Research (Elements in Quantitative and Computational Methods for the Social Sciences) (in English)
Philip D. Waggoner
(Author)
·
Cambridge University Press
· Paperback
Unsupervised Machine Learning for Clustering in Political and Social Research (Elements in Quantitative and Computational Methods for the Social Sciences) (in English) - Waggoner, Philip D.
$ 19.68
$ 22.00
You save: $ 2.32
Choose the list to add your product or create one New List
✓ Product added successfully to the Wishlist.
Go to My WishlistsIt will be shipped from our warehouse between
Monday, July 01 and
Tuesday, July 02.
You will receive it anywhere in United States between 1 and 3 business days after shipment.
Synopsis "Unsupervised Machine Learning for Clustering in Political and Social Research (Elements in Quantitative and Computational Methods for the Social Sciences) (in English)"
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.
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
All books in our catalog are Original.
The book is written in English.
The binding of this edition is Paperback.
✓ Producto agregado correctamente al carro, Ir a Pagar.