Introduction

Large amounts of data are collected every day from satellite images, bio-medical, security, marketing, web search, geo-spatial or other automatic equipment. Mining knowledge from these big data far exceeds human’s abilities.

Clustering is one of the important data mining methods for discovering knowledge in multidimensional data. The goal of clustering is to identify pattern or groups of similar objects within a data set of interest.

In the litterature, it is referred as “pattern recognition” or “unsupervised machine learning” - “unsupervised” because we are not guided by a priori ideas of which variables or samples belong in which clusters. “Learning” because the machine algorithm “learns” how to cluster.

Cluster analysis is popular in many fields, including:

• In cancer research for classifying patients into subgroups according their gene expression profile. This can be useful for identifying the molecular profile of patients with good or bad prognostic, as well as for understanding the disease.

• In marketing for market segmentation by identifying subgroups of customers with similar profiles and who might be receptive to a particular form of advertising.

• In City-planning for identifying groups of houses according to their type, value and location.

A preview of the first 38 pages of the file is available by clicking this link: Practical Guide to Cluster Analysis in R (preview).

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Key features of this book

Although there are several good books on unsupervised machine learning/clustering and related topics, we felt that many of them are either too high-level, theoretical or too advanced. Our goal was to write a practical guide to cluster analysis, elegant visualization and interpretation.

The main parts of the book include:

• distance measures,
• partitioning clustering,
• hierarchical clustering,
• cluster validation methods, as well as,
• advanced clustering methods such as fuzzy clustering, density-based clustering and model-based clustering.

The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers.

Key features:

• Covers clustering algorithm and implementation
• Key mathematical concepts are presented
• Short, self-contained chapters with practical examples. This means that, you don’t need to read the different chapters in sequence.

How this book is organized?

This book contains 5 parts. Part I (Chapter 1 - 3) provides a quick introduction to R (chapter 1) and presents required R packages and data format (Chapter 2) for clustering analysis and visualization.

The classification of objects, into clusters, requires some methods for measuring the distance or the (dis)similarity between the objects. Chapter 3 covers the common distance measures used for assessing similarity between observations.

Part II starts with partitioning clustering methods, which include:

• K-means clustering (Chapter 4),
• K-Medoids or PAM (partitioning around medoids) algorithm (Chapter 5) and
• CLARA algorithms (Chapter 6).

Partitioning clustering approaches subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst.

cluster analysis in R

In Part III, we consider agglomerative hierarchical clustering method, which is an alternative approach to partitionning clustering for identifying groups in a data set. It does not require to pre-specify the number of clusters to be generated. The result of hierarchical clustering is a tree-based representation of the objects, which is also known as dendrogram (see the figure below).

In this part, we describe how to compute, visualize, interpret and compare dendrograms:

• Agglomerative clustering (Chapter 7)
  • Algorithm and steps
  • Verify the cluster tree
  • Cut the dendrogram into different groups
• Compare dendrograms (Chapter 8)
  • Visual comparison of two dendrograms
  • Correlation matrix between a list of dendrograms
• Visualize dendrograms (Chapter 9)
  • Case of small data sets
  • Case of dendrogram with large data sets: zoom, sub-tree, PDF
  • Customize dendrograms using dendextend
• Heatmap: static and interactive (Chapter 10)
  • R base heat maps
  • Pretty heat maps
  • Interactive heat maps
  • Complex heatmap
  • Real application: gene expression data

In this section, you will learn how to generate and interpret the following plots.

• Standard dendrogram with filled rectangle around clusters:

cluster analysis in R

• Compare two dendrograms:

cluster analysis in R

• Heatmap:

cluster analysis in R

Part IV describes clustering validation and evaluation strategies, which consists of measuring the goodness of clustering results. Before applying any clustering algorithm to a data set, the first thing to do is to assess the clustering tendency. That is, whether applying clustering is suitable for the data. If yes, then how many clusters are there. Next, you can perform hierarchical clustering or partitioning clustering (with a pre-specified number of clusters). Finally, you can use a number of measures, described in this chapter, to evaluate the goodness of the clustering results.

The different chapters included in part IV are organized as follow:

• Assessing clustering tendency (Chapter 11)

• Determining the optimal number of clusters (Chapter 12)

• Cluster validation statistics (Chapter 13)

• Choosing the best clustering algorithms (Chapter 14)

• Computing p-value for hierarchical clustering (Chapter 15)

In this section, you’ll learn how to create and interpret the plots hereafter.

• Visual assessment of clustering tendency (left panel): Clustering tendency is detected in a visual form by counting the number of square shaped dark blocks along the diagonal in the image.
• Determine the optimal number of clusters (right panel) in a data set using the gap statistics.

cluster analysis in R

• Cluster validation using the silhouette coefficient (Si): A value of Si close to 1 indicates that the object is well clustered. A value of Si close to -1 indicates that the object is poorly clustered. The figure below shows the silhouette plot of a k-means clustering.

cluster analysis in R

Part V presents advanced clustering methods, including:

• Hierarchical k-means clustering (Chapter 16)
• Fuzzy clustering (Chapter 17)
• Model-based clustering (Chapter 18)
• DBSCAN: Density-Based Clustering (Chapter 19)

The hierarchical k-means clustering is an hybrid approach for improving k-means results.

In Fuzzy clustering, items can be a member of more than one cluster. Each item has a set of membership coefficients corresponding to the degree of being in a given cluster.

In model-based clustering, the data are viewed as coming from a distribution that is mixture of two ore more clusters. It finds best fit of models to data and estimates the number of clusters.

The density-based clustering (DBSCAN is a partitioning method that has been introduced in Ester et al. (1996). It can find out clusters of different shapes and sizes from data containing noise and outliers.

cluster analysis in R

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: https://www.sthda.com/english/wiki/r-packages

Recently, he published two books on data visualization:

1. 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).

How this book is organized?

This book contains 6 parts. The first part provides a quick introduction to R (chapter 1) and to ggplot2 plotting system (chapter 1).

In the second part (chapter 3 - 11), we described the different graphs for visualizing one continous/discrete variable: area plots, density plots, histogram plots, frequency polygon, dot plots, ECDF and QQ plots.

ggplot2: Guide to Create Beautiful Graphics in R

Part III provides quick-start guides for plotting two continuous/discretes variables, including :

• Scatter plots (chapter 12)
• Continuous bivariate distribution (chapter 13)
• Jitter plots of two discretes variables (chapter 14)

ggplot2: Guide to Create Beautiful Graphics in R

Part IV (chapter 15 - 22 ) describes how to draw and customize: box plots, violin plots, dot plots, strip charts, line plots, bar plots and pie charts.

ggplot2: Guide to Create Beautiful Graphics in R

Part V of the book covers how to change graphical parameters including:

• Main title, axis labels and legend titles (chapter 24)
• Legend position and appearance (chapter 25)
• Colors (chapter 26)
• Point shapes, colors and size (chapter 27)
• Line types (chapter 28)
• Axis limits: minimum and maximum values (chapter 29)
• Axis transformations: log and sqrt (chapter 30)
• Date axes (chapter 31)
• Axis ticks : customize tick marks and labels (chapter 32)
• Themes and background colors (chapter 33)
• Add text annotations to a graph (chapter 34)
• Add straight lines to a plot: horizontal, vertical and regression lines (chapter 35)
• Rotate a plot (chapter 36)
• Facets: split a plot into a matrix of panels (chapter 37)
• Position adjustements (chapter 38)
• Coordinate systems (chapter 39)

Part VI describes some extensions of ggplot2 including:

• Arranging multiple graphs on the same page (chapter 40)
• Correlation matrix visualization (chapter 41)
• Plotting survival curves (chapter 42)

Survival curves and correlation matrix:

ggplot2: Guide to Create Beautiful Graphics in R

Mixing multiple plots on the same page:

ggplot2: Guide to Create Beautiful Graphics in R

Each chapter is organized as an independent quick start guide. This means that, you don’t need to read the different chapters in sequence.

For each chapter, the covered ggplot2 key functions are generally mentioned at the beginning. The used data are described and many examples of R codes and graphics are provided.

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), 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).

How this book is organized?

This book provides a complete guide for visualizing a data in 3 dimensions (3D) using R software. It contains 2 main parts and 7 chapters describing how to draw static and interactive 3D plots using R software.

• The chapter 1 is about data preparation for 3D plot
• In chapter 2, we describe how to create easily basic static 3D scatter plots. We provide R codes for changing: 1) main and axis titles; 2) the appearance of the plot (point colors, labels and shapes, legend position, …)
• Chapter 3 presents how to create advanced static 3D plots including 3D scatter plots with confidence interval, 3D line plots, 3D texts, 3D barplots, 3D histograms and 3D arrows.
• Chapter 4 describes the required package for drawing interactive 3D plots.
• In chapter 5, we show how to transform easily an existing static 3D plot into aninteractive 3D plot.
• Chapter 6 provides many examples of R codes for creating interactive 3D scatter plotswith 3D regression surfaces and concentration ellipsoids. We describe also how to exportthese graphs as png or pdf files.
• Chapter 7 presents a complete guide to RGL 3D visualization device system. We provide also R codes for creating a movie from RGL 3D scene and for exporting plot into an interactive HTML web file.

Each chapter is organized as an independent quick start guide. This means that, you don’tneed to read the different chapters in sequence.

Download the ebook through payhip:

Download the ebook through google play:

Order a physical copy from amazon:

Some examples of graphs, described in this book, are shown below:

Complete Guide to 3D Plots in R

Complete Guide to 3D Plots in R

Complete Guide to 3D Plots in R

Complete Guide to 3D Plots in R

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), 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).

Download the ebook through payhip: