Practical Guide to Cluster Analysis in R: Unsupervised Machine Learning

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STHDA, 2017/08/23 - 187 ページ

 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.

 

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目次

II Partitioning Clustering
15
III Hierarchical Clustering
41
IV Cluster Validation
91
V Advanced Clustering
127
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著者について (2017)

Alboukadel Kassambara is a PhD in Bioinformatics and Cancer Biology. He works since many years on genomic data analysis and visualization (read more: http://www.alboukadel.com/).

He has work experiences in statistical and computational methods to identify prognostic and predictive biomarker signatures through integrative analysis of large-scale genomic and clinical data sets.

He created a bioinformatics web-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 training website on data science, named 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 many popular R packages for:

multivariate data analysis (factoextra, http://www.sthda.com/english/rpkgs/factoextra),survival analysis (survminer, http://www.sthda.com/english/rpkgs/survminer/),correlation analysis (ggcorrplot, http://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2),creating publication ready plots in R (ggpubr, http://www.sthda.com/english/rpkgs/ggpubr).

Recently, he published three books on data analysis and visualization:

Practical Guide to Cluster Analysis in R (https://goo.gl/DmJ5y5)Guide to Create Beautiful Graphics in R (https://goo.gl/vJ0OYb).Complete Guide to 3D Plots in R (https://goo.gl/v5gwl0).

 

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