book

Applied Unsupervised Learning with R

Name: Applied Unsupervised Learning with R
ISBN: 9781789956399

by Alok Malik, Bradford Tuckfield

March 2019

Intermediate to advanced

320 pages

5h 28m

English

Packt Publishing

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Preface
About the BookAbout the AuthorsElevator PitchKey FeaturesDescriptionLearning ObjectivesAudienceApproachHardware RequirementsSoftware RequirementsConventionsInstallation and SetupInstalling R on WindowsInstalling R on macOS XInstalling R on Linux
Chapter 1
Introduction to Clustering MethodsIntroductionIntroduction to Clustering Uses of ClusteringIntroduction to the Iris DatasetExercise 1: Exploring the Iris DatasetTypes of ClusteringIntroduction to k-means ClusteringEuclidean DistanceManhattan DistanceCosine DistanceThe Hamming Distancek-means Clustering AlgorithmSteps to Implement k-means ClusteringExercise 2: Implementing k-means Clustering on the Iris DatasetActivity 1: k-means Clustering with Three ClustersIntroduction to k-means Clustering with Built-In Functionsk-means Clustering with Three ClustersExercise 3: k-means Clustering with R LibrariesIntroduction to Market SegmentationExercise 4: Exploring the Wholesale Customer DatasetActivity 2: Customer Segmentation with k-means Introduction to k-medoids ClusteringThe k-medoids Clustering Algorithmk-medoids Clustering CodeExercise 5: Implementing k-medoid Clusteringk-means Clustering versus k-medoids ClusteringActivity 3: Performing Customer Segmentation with k-medoids ClusteringDeciding the Optimal Number of ClustersTypes of Clustering MetricsSilhouette ScoreExercise 6: Calculating the Silhouette ScoreExercise 7: Identifying the Optimum Number of ClustersWSS/Elbow MethodExercise 8: Using WSS to Determine the Number of ClustersThe Gap StatisticExercise 9: Calculating the Ideal Number of Clusters with the Gap StatisticActivity 4: Finding the Ideal Number of Market SegmentsSummary
Chapter 2
Advanced Clustering MethodsIntroductionIntroduction to k-modes ClusteringSteps for k-Modes ClusteringExercise 10: Implementing k-modes ClusteringActivity 5: Implementing k-modes Clustering on the Mushroom DatasetIntroduction to Density-Based Clustering (DBSCAN)Steps for DBSCAN Exercise 11: Implementing DBSCAN Uses of DBSCAN Activity 6: Implementing DBSCAN and Visualizing the ResultsIntroduction to Hierarchical ClusteringTypes of Similarity MetricsSteps to Perform Agglomerative Hierarchical ClusteringExercise 12: Agglomerative Clustering with Different Similarity MeasuresDivisive ClusteringSteps to Perform Divisive ClusteringExercise 13: Performing DIANA Clustering Activity 7: Performing Hierarchical Cluster Analysis on the Seeds DatasetSummary
Chapter 3
Probability DistributionsIntroductionBasic Terminology of Probability DistributionsUniform DistributionExercise 14: Generating and Plotting Uniform Samples in RNormal DistributionExercise 15: Generating and Plotting a Normal Distribution in RSkew and KurtosisLog-Normal DistributionsExercise 16: Generating a Log-Normal Distribution from a Normal DistributionThe Binomial DistributionExercise 17: Generating a Binomial DistributionThe Poisson DistributionThe Pareto DistributionIntroduction to Kernel Density EstimationKDE AlgorithmExercise 18: Visualizing and Understanding KDEExercise 19: Studying the Effect of Changing Kernels on a DistributionActivity 8: Finding the Standard Distribution Closest to the Distribution of Variables of the Iris DatasetIntroduction to the Kolmogorov-Smirnov TestThe Kolmogorov-Smirnov Test AlgorithmExercise 20: Performing the Kolmogorov-Smirnov Test on Two SamplesActivity 9: Calculating the CDF and Performing the Kolmogorov-Smirnov Test with the Normal DistributionSummary
Chapter 4
Dimension ReductionIntroductionThe Idea of Dimension ReductionExercise 21: Examining a Dataset that Contains the Chemical Attributes of Different WinesImportance of Dimension ReductionMarket Basket AnalysisExercise 22: Data Preparation for the Apriori AlgorithmExercise 23: Passing through the Data to Find the Most Common BasketsExercise 24: More Passes through the DataExercise 25: Generating Associative Rules as the Final Step of the Apriori AlgorithmPrincipal Component AnalysisLinear Algebra RefresherMatricesVarianceCovarianceExercise 26: Examining Variance and Covariance on the Wine DatasetEigenvectors and EigenvaluesThe Idea of PCAExercise 27: Performing PCAExercise 28: Performing Dimension Reduction with PCAActivity 10: Performing PCA and Market Basket Analysis on a New DatasetSummary
Chapter 5
Data Comparison MethodsIntroductionHash FunctionsExercise 29: Creating and Using a Hash FunctionExercise 30: Verifying Our Hash FunctionAnalytic SignaturesExercise 31: Perform the Data Preparation for Creating an Analytic Signature for an Image Exercise 32: Creating a Brightness Comparison FunctionExercise 33: Creating a Function to Compare Image Sections to All of the Neighboring SectionsExercise 34: Creating a Function that Generates an Analytic Signature for an ImageActivity 11: Creating an Image Signature for a Photograph of a PersonComparison of SignaturesActivity 12: Creating an Image Signature for the Watermarked ImageApplying Other Unsupervised Learning Methods to Analytic Signatures Latent Variable Models – Factor AnalysisExercise 35: Preparing for Factor AnalysisLinear Algebra behind Factor AnalysisExercise 36: More Exploration with Factor Analysis Activity 13: Performing Factor AnalysisSummary
Chapter 6
Anomaly DetectionIntroductionUnivariate Outlier DetectionExercise 37: Performing an Exploratory Visual Check for Outliers Using R's boxplot FunctionExercise 38: Transforming a Fat-Tailed Dataset to Improve Outlier ClassificationExercise 39: Finding Outliers without Using R's Built-In boxplot FunctionExercise 40: Detecting Outliers Using a Parametric MethodMultivariate Outlier DetectionExercise 41: Calculating Mahalanobis DistanceDetecting Anomalies in ClustersOther Methods for Multivariate Outlier DetectionExercise 42: Classifying Outliers based on Comparisons of Mahalanobis DistancesDetecting Outliers in Seasonal DataExercise 43: Performing Seasonality ModelingExercise 44: Finding Anomalies in Seasonal Data Using a Parametric MethodContextual and Collective AnomaliesExercise 45: Detecting Contextual AnomaliesExercise 46: Detecting Collective AnomaliesKernel DensityExercise 47: Finding Anomalies Using Kernel Density EstimationContinuing in Your Studies of Anomaly DetectionActivity 14: Finding Univariate Anomalies Using a Parametric Method and a Non-parametric MethodActivity 15: Using Mahalanobis Distance to Find AnomaliesSummary
Appendix
Chapter 1: Introduction to Clustering MethodsActivity 1: k-means Clustering with Three ClustersActivity 2: Customer Segmentation with k-means Activity 3: Performing Customer Segmentation with k-medoids Clustering Activity 4: Finding the Ideal Number of Market SegmentsChapter 2: Advanced Clustering MethodsActivity 5: Implementing k-modes Clustering on the Mushroom DatasetActivity 6: Implementing DBSCAN and Visualizing the ResultsActivity 7: Performing a Hierarchical Cluster Analysis on the Seeds DatasetChapter 3: Probability DistributionsActivity 8: Finding the Standard Distribution Closest to the Distribution of Variables of the Iris DatasetActivity 9: Calculating the CDF and Performing the Kolmogorov-Simonov Test with the Normal DistributionChapter 4: Dimension ReductionActivity 10: Performing PCA and Market Basket Analysis on a New DatasetChapter 5: Data Comparison MethodsActivity 11: Create an Image Signature for a Photograph of a PersonActivity 12: Create an Image Signature for the Watermarked ImageActivity 13: Performing Factor AnalysisChapter 6: Anomaly DetectionActivity 14: Finding Univariate Anomalies Using a Parametric Method and a Non-parametric MethodActivity 15: Using Mahalanobis Distance to Find Anomalies

Content preview from Applied Unsupervised Learning with R

Chapter 4 Dimension Reduction

Learning Objectives

By the end of this chapter, you will be able to:

Apply different dimension reduction techniques
Execute market basket analysis using the Apriori algorithm
Perform principal component analysis on a dataset

In this chapter, we will have a look at different dimension reduction techniques.

Introduction

This chapter presents techniques for unsupervised learning that accomplish something called dimension reduction. First, we will discuss what a dimension is, why we want to avoid having too many dimensions, and the basic idea of dimension reduction. The chapter then covers two dimension reduction techniques in detail: market basket analysis and Principal Component Analysis (PCA). Market basket ...

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Applied Unsupervised Learning with R

by Alok Malik, Bradford Tuckfield

Chapter 4

Dimension Reduction

Learning Objectives

Introduction

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