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Practical Guide To Cluster Analysis In R Unsupervised Machine Learning Alboukadel Kassambara

Name: Practical Guide To Cluster Analysis In R Unsupervised Machine Learning Alboukadel Kassambara
SKU: BELL-5852930
Price: 31.00 USD
Availability: InStock
Rating: 4.4 (72 reviews)

SKU: BELL-5852930

$ 31.00 ~~$ 45.00~~ (-31%)

4.4

72 reviews

Practical Guide To Cluster Analysis In R Unsupervised Machine Learning Alboukadel Kassambara instant download after payment.

Publisher: STHDA

File Extension: PDF

File size: 4.99 MB

Pages: 187

Author: Alboukadel Kassambara

ISBN: 9781542462709, 1542462703

Language: English

Year: 2017

Product desciption

Practical Guide To Cluster Analysis In R Unsupervised Machine Learning Alboukadel Kassambara by Alboukadel Kassambara 9781542462709, 1542462703 instant download after payment.

Although there are several good books on unsupervised machine learning, we felt that many of them are too theoretical. This book provides practical guide to cluster analysis, elegant visualization and interpretation. It contains 5 parts. Part I provides a quick introduction to R and presents required R packages, as well as, data formats and dissimilarity measures for cluster analysis and visualization. Part II covers partitioning clustering methods, which subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst. Partitioning clustering approaches include: K-means, K-Medoids (PAM) and CLARA algorithms. In Part III, we consider hierarchical clustering method, which is an alternative approach to partitioning clustering. The result of hierarchical clustering is a tree-based representation of the objects called dendrogram. In this part, we describe how to compute, visualize, interpret and compare dendrograms. Part IV describes clustering validation and evaluation strategies, which consists of measuring the goodness of clustering results. Among the chapters covered here, there are: Assessing clustering tendency, Determining the optimal number of clusters, Cluster validation statistics, Choosing the best clustering algorithms and Computing p-value for hierarchical clustering. Part V presents advanced clustering methods, including: Hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and Density-based clustering.