Apache Mahout Clustering Designs电子书

Apache Mahout Clustering Designs

Table of Contents

Apache Mahout Clustering Designs

Credits

About the Author

About the Reviewers

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Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Understanding Clustering

The clustering concept

Application of clustering

Understanding distance measures

Understanding different clustering techniques

Hierarchical methods

The partitioning method

The density-based method

Probabilistic clustering

Algorithm support in Mahout

Clustering algorithms in Mahout

Installing Mahout

Building Mahout code using Maven

Setting up the development environment using Eclipse

Setting up Mahout for Windows users

Preparing data for use with clustering techniques

Summary

2. Understanding K-means Clustering

Learning K-means

Running K-means on Mahout

Dataset selection

Executing K-means

The clusterdump result

Visualizing clusters

Summary

3. Understanding Canopy Clustering

Running Canopy clustering on Mahout

The Canopy generation phase

The Canopy clustering phase

Running Canopy clustering

Using the Canopy output for K-means

Visualizing clusters

Working with CSV files

Summary

4. Understanding the Fuzzy K-means Algorithm Using Mahout

Learning Fuzzy K-means clustering

Running Fuzzy K-means on Mahout

Dataset

Creating a vector for the dataset

Vector reader

Visualizing clusters

Summary

5. Understanding Model-based Clustering

Learning model-based clustering

Understanding Dirichlet clustering

Topic modeling

Running LDA using Mahout

Dataset selection

Steps to execute CVB (LDA)

Summary

6. Understanding Streaming K-means

Learning Streaming K-means

The Streaming step

The BallKMeans step

Using Mahout for streaming K-means

Dataset selection

Converting CSV to a vector file

Running Streaming K-means

Summary

7. Spectral Clustering

Understanding spectral clustering

Affinity (similarity) graph

Getting graph Laplacian from the affinity matrix

Eigenvectors and eigenvalues

The spectral clustering algorithm

Normalized spectral clustering

Mahout implementation of spectral clustering

Summary

8. Improving Cluster Quality

Evaluating clusters

Extrinsic methods

Intrinsic methods

Using DistanceMeasure interface

Summary

9. Creating a Cluster Model for Production

Preparing the dataset

Launching the Mahout job on the cluster

Performance tuning for the job

Summary

Index