Name: Unsupervised Machine Learning for Clustering in Political and Social Research (Elements in Quantitative and Computational Methods for the Social Sciences) (en Inglés)
Price: 115986.00 COP
Availability: InStock
Author: Waggoner, Philip D.
ISBN: 9781108793384

portada Unsupervised Machine Learning for Clustering in Political and Social Research (Elements in Quantitative and Computational Methods for the Social Sciences) (en Inglés)

Formato

Libro Físico

Autor

Philip D. Waggoner

Editorial

Cambridge University Press

Año

2021

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

22.9 x 15.2 x 0.4 cm

Peso

0.10 kg.

ISBN13

9781108793384

Categorías

Ciencias políticas

Unsupervised Machine Learning for Clustering in Political and Social Research (Elements in Quantitative and Computational Methods for the Social Sciences) (en Inglés)

Philip D. Waggoner (Autor) · Cambridge University Press · Tapa Blanda

Libro Nuevo

$ 115.986

$ 231.972

Ahorras: $ 115.986

50% descuento

Envío normal

Origen: España (Costos de importación incluídos en el precio)

Se enviará desde nuestra bodega entre el Jueves 18 de Julio y el Martes 30 de Julio.

Lo recibirás en cualquier lugar de Colombia entre 1 y 5 días hábiles luego del envío.

Reseña del libro "Unsupervised Machine Learning for Clustering in Political and Social Research (Elements in Quantitative and Computational Methods for the Social Sciences) (en Inglés)"

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.