Practical Guide to Cluster Analysis in R Unsupervised Machine Learning 1st Edition by Alboukadel Kassambara ISBN 1542462703 978-1542462709

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Product details:

ISBN 10: 1542462703

ISBN 13:  978-1542462709

Author: Alboukadel Kassambara

 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.

Table of contents:

 1 Introduction to R

2 Data Preparation and R Packages 

3 Clustering Distance Measures

4 Introduction

5 K-Means Clustering

6 K-Medoids

7 CLARA Clustering Large Applications

8 Introduction

9 Agglomerative Clustering

10 Divisive Hierarchical Clustering

11 Comparing Dendrograms

12 Visualizing Dendrograms

13 Heatmap: Static and Interactive

14 Introduction

15 Assessing Clustering Tendency

16 Determining the Optimal Number of Clusters

17 Cluster Validation Statistics

18 Choosing the Best Clustering Algorithms

19 Computing P-value for Hierarchical Clustering

20 Introduction

21 Hierarchical K-Means Clustering

22 Fuzzy Clustering

23 Model-Based Clustering

24 DBSCAN: Density-Based Clustering

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Tags: Alboukadel Kassambara, Practical Guide, Cluster Analysis, Unsupervised Machine Learning