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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.
- Sales Rank: #238436 in Books
- Published on: 2017-01-09
- Original language: English
- Dimensions: 10.00" h x .45" w x 8.00" l,
- Binding: Paperback
- 188 pages
About the Author
Alboukadel Kassambara is a PhD in Bioinformatics and Cancer Biology. He works since many years on genomic data analysis and visualization. He created a bioinformatics tool named GenomicScape (www.genomicscape.com) which is an easy-to-use web tool for gene expression data analysis and visualization. He developed also a website called STHDA (Statistical Tools for High-throughput Data Analysis, www.sthda.com/english), which contains many tutorials on data analysis and visualization using R software and packages. He is the author of the R packages survminer (for analyzing and drawing survival curves), ggcorrplot (for drawing correlation matrix using ggplot2) and factoextra (to easily extract and visualize the results of multivariate analysis such PCA, CA, MCA and clustering). You can learn more about these packages at: http://www.sthda.com/english/wiki/r-packages. Recently, he published two books on data visualization: i) Guide to Create Beautiful Graphics in R (at: https://goo.gl/vJ0OYb); 2) Complete Guide to 3D Plots in R (at: https://goo.gl/v5gwl0).
Most helpful customer reviews
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GREAT book on cluster analysis in R
By a reader
This is a very practical guide to cluster analysis. The book is an extremely easy and straightforward read which I went through in all of a couple of hours. You will find that you can run every analysis in the book by following the clear, uncluttered programming code. The visualization examples are excellent, as are the discussions of diagnostics for the number of clusters and for assessing cluster tendency There were a couple of topics missing that might improve a second edition: (1) while a chapter on advanced methods covers model-based clustering for metric dependent variables, there is no corresponding coverage of the poLCA package for categorical supervising variables, (2) while the book covers agglomerative hierarchical clustering, it skips divisive hierarchical clustering, available in the package diana.
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