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Home > Data > Machine Learning > Apache Mahout Essentials
Apache Mahout Essentials
Apache Mahout Essentials

Apache Mahout Essentials: Implement top-notch machine learning algorithms for classification, clustering, and recommendations with Apache Mahout

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Apache Mahout Essentials

Chapter 2. Clustering

This chapter explains the clustering technique in machine learning and its implementation using Apache Mahout.

The K-Means clustering algorithm is explained in detail with both Java and command-line examples (sequential and parallel executions), and other important clustering algorithms, such as Fuzzy K-Means, canopy clustering, and spectral K-Means are also explored.

In this chapter, we will cover the following topics:

  • Unsupervised learning and clustering
  • Applications of clustering
  • Types of clustering
  • K-Means clustering
  • K-Means clustering with MapReduce
  • Other clustering algorithms
  • Text clustering
  • Optimizing clustering performance

Unsupervised learning and clustering

Information is a key driver for any type of organization. However, with the rapid growth in the volume of data, valuable information may be hidden and go unnoticed due to the lack of effective data processing and analyzing mechanisms.

Clustering is an unsupervised learning mechanism that can find the hidden patterns and structures in data by finding data points that are similar to each other. No prelabeling is required. So, you can organize data using clustering with little or no human intervention.

For example, let's say you are given a collection of balls of different sizes without any category labels, such as big and small, attached to them; you should be able to categorize them using clustering by considering their attributes, such as radius and weight, for similarity.

In this chapter, you will learn how to use Apache Mahout to perform clustering using different algorithms.

Applications of clustering

Clustering has many applications in different domains, such as biology, business, and information retrieval. A few of them are shown in the following image:

Applications of clustering

Computer vision and image processing

Clustering techniques are widely used in the computer vision and image processing domain. Clustering is used for image segmentation in medical image processing for computer aided disease (CAD) diagnosis. One specific area is breast cancer detection.

In breast cancer detection, a mammogram is clustered into several parts for further analysis, as shown in the following image. The regions of interest for signs of breast cancer in the mammogram can be identified using the K-Means algorithm, which is explained later in this chapter.

Image features such as pixels, colors, intensity, and texture are used during clustering:

Computer vision and image processing

Types of clustering

Clustering can be divided into different categories based on different criteria.

Hard clustering versus soft clustering

Clustering techniques can be divided into hard clustering and soft clustering based on the cluster's membership.

In hard clustering, a given data point in n-dimensional space only belongs to one cluster. This is also known as exclusive clustering. The K-Means clustering mechanism is an example of hard clustering.

A given data point can belong to more than one cluster in soft clustering. This is also known as overlapping clustering. The Fuzzy K-Means algorithm is a good example of soft clustering. A visual representation of the difference between hard clustering and soft clustering is given in the following figure:

Hard clustering versus soft clustering

Flat clustering versus hierarchical clustering

In hierarchical clustering, a hierarchy of clusters is built using the top-down (divisive) or bottom-up (agglomerative) approach. This is more informative and accurate than flat clustering, which...

K-Means clustering

K-Means clustering is a simple and fast clustering algorithm that has been widely adopted in many problem domains. In this chapter, we will give a detailed explanation of the K-Means algorithm, as it will provide the base for other algorithms. K-Means clustering assigns data points to k number of clusters (cluster centroids) by minimizing the distance from the data points to the cluster centroids.

Let's consider a simple scenario where we need to cluster people based on their size (height and weight are the selected attributes) and different colors (clusters):

K-Means clustering

We can plot this problem in two-dimensional space, as shown in the following figure and solve it using the K-Means algorithm:

K-Means clustering

Getting your hands dirty!

Let's move on to a real implementation of the K-Means algorithm using Apache Mahout. The following are the different ways in which you can run algorithms in Apache Mahout:

  • Sequential
  • MapReduce

You can execute the algorithms using a command line (by calling the...

Distance measure

The clustering problem is based on evaluating the distance between data points. The distance measure is an indicator of the similarity of the data points. For any clustering algorithm, you need to make a decision on the appropriate distance measure for your context. Essentially, the distance measure is more important for accuracy than the number of clusters.

Further, the criteria for choosing the right distance measure depends on the application domain and the dataset, so it is important to understand the different distance measures available in Apache Mahout. A few important distance measures are explained in the following section. The distance measure is visualized using a two-dimensional visualization here.

The Euclidean distance is not suitable if the magnitude of possible values for each feature varies drastically (if all the features need to be assessed equally):

Euclidean distance

Class

org.apache.mahout.common.distance.EuclideanDistanceMeasure

Formula
Distance measure
Distance measure

Squared...

Unsupervised learning and clustering

Information is a key driver for any type of organization. However, with the rapid growth in the volume of data, valuable information may be hidden and go unnoticed due to the lack of effective data processing and analyzing mechanisms.

Clustering is an unsupervised learning mechanism that can find the hidden patterns and structures in data by finding data points that are similar to each other. No prelabeling is required. So, you can organize data using clustering with little or no human intervention.

For example, let's say you are given a collection of balls of different sizes without any category labels, such as big and small, attached to them; you should be able to categorize them using clustering by considering their attributes, such as radius and weight, for similarity.

In this chapter, you will learn how to use Apache Mahout to perform clustering using different algorithms.

Applications of clustering

Clustering has many applications in different domains, such as biology, business, and information retrieval. A few of them are shown in the following image:

Computer vision and image processing

Clustering techniques are widely used in the computer vision and image processing domain. Clustering is used for image segmentation in medical image processing for computer aided disease (CAD) diagnosis. One specific area is breast cancer detection.

In breast cancer detection, a mammogram is clustered into several parts for further analysis, as shown in the following image. The regions of interest for signs of breast cancer in the mammogram can be identified using the K-Means algorithm, which is explained later in this chapter.

Image features such as pixels, colors, intensity, and texture are used during clustering:

Types of clustering

Clustering can be divided into different categories based on different criteria.

Hard clustering versus soft clustering

Clustering techniques can be divided into hard clustering and soft clustering based on the cluster's membership.

In hard clustering, a given data point in n-dimensional space only belongs to one cluster. This is also known as exclusive clustering. The K-Means clustering mechanism is an example of hard clustering.

A given data point can belong to more than one cluster in soft clustering. This is also known as overlapping clustering. The Fuzzy K-Means algorithm is a good example of soft clustering. A visual representation of the difference between hard clustering and soft clustering is given in the following figure:

Flat clustering versus hierarchical clustering

In hierarchical clustering, a hierarchy of clusters is built using the top-down (divisive) or bottom-up (agglomerative) approach. This is more informative and accurate than flat clustering, which is...

K-Means clustering

K-Means clustering is a simple and fast clustering algorithm that has been widely adopted in many problem domains. In this chapter, we will give a detailed explanation of the K-Means algorithm, as it will provide the base for other algorithms. K-Means clustering assigns data points to k number of clusters (cluster centroids) by minimizing the distance from the data points to the cluster centroids.

Let's consider a simple scenario where we need to cluster people based on their size (height and weight are the selected attributes) and different colors (clusters):

We can plot this problem in two-dimensional space, as shown in the following figure and solve it using the K-Means algorithm:

Getting your hands dirty!

Let's move on to a real implementation of the K-Means algorithm using Apache Mahout. The following are the different ways in which you can run algorithms in Apache Mahout:

  • Sequential

  • MapReduce

You can execute the algorithms using a command line (by calling the correct bin...

Distance measure

The clustering problem is based on evaluating the distance between data points. The distance measure is an indicator of the similarity of the data points. For any clustering algorithm, you need to make a decision on the appropriate distance measure for your context. Essentially, the distance measure is more important for accuracy than the number of clusters.

Further, the criteria for choosing the right distance measure depends on the application domain and the dataset, so it is important to understand the different distance measures available in Apache Mahout. A few important distance measures are explained in the following section. The distance measure is visualized using a two-dimensional visualization here.

The Euclidean distance is not suitable if the magnitude of possible values for each feature varies drastically (if all the features need to be assessed equally):

Euclidean distance

Class

org.apache.mahout.common.distance.EuclideanDistanceMeasure

Formula

Squared...

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Description

If you are a Java developer or data scientist, haven't worked with Apache Mahout before, and want to get up to speed on implementing machine learning on big data, then this is the perfect guide for you.

What you will learn

  • Get started with the fundamentals of Big Data, batch, and realtime data processing with an introduction to Mahout and its applications
  • Understand the key machine learning concepts behind algorithms in Apache Mahout
  • Apply machine learning algorithms provided by Apache Mahout in realworld practical scenarios
  • Implement and evaluate widelyused clustering, classification, and recommendation algorithms using Apache Mahout
  • Discover tips and tricks to improve the accuracy and performance of your results
  • Set up Apache Mahout in a production environment with Apache Hadoop
  • Glance at the Spark DSL advancements in Apache Mahout 1.0
  • Provide dynamic and interactive data visualizations for Apache Mahout
  • Build a recommendation engine for realtime use cases and use userbased and itembased recommendation algorithms

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jun 19, 2015
Length: 164 pages
Edition : 1st
Language : English
ISBN-13 : 9781783555000
Vendor :
Apache
Category :
Data
Languages :
Java
Concepts :
Machine Learning
Tools :
Mahout

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Product Details

Publication date : Jun 19, 2015
Length: 164 pages
Edition : 1st
Language : English
ISBN-13 : 9781783555000
Vendor :
Apache
Category :
Data
Languages :
Java
Concepts :
Machine Learning
Tools :
Mahout

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Table of Contents

7 Chapters
1. Introducing Apache Mahout Chevron down icon Chevron up icon
1. Introducing Apache Mahout
Machine learning in a nutshell
Machine learning applications
Machine learning libraries
The story so far
Apache Mahout
How Apache Mahout works?
From Hadoop MapReduce to Spark
When is it appropriate to use Apache Mahout?
Summary
2. Clustering Chevron down icon Chevron up icon
2. Clustering
Unsupervised learning and clustering
Applications of clustering
Types of clustering
K-Means clustering
Distance measure
K-Means clustering with MapReduce
Additional clustering algorithms
Text clustering
Optimizing clustering performance
Summary
3. Regression and Classification Chevron down icon Chevron up icon
3. Regression and Classification
Supervised learning
Predictive analytics' techniques
Classification versus regression
Linear regression with Apache Spark
Logistic regression with SGD
The Naïve Bayes algorithm
Hidden Markov Model
Summary
4. Recommendations Chevron down icon Chevron up icon
4. Recommendations
Collaborative versus content-based filtering
User-based recommenders
Item-based recommenders
Matrix factorization-based recommenders
Singular value decomposition
Summary
5. Apache Mahout in Production Chevron down icon Chevron up icon
5. Apache Mahout in Production
Introduction
Apache Mahout with Hadoop
Setting up Hadoop
Monitoring Hadoop
Setting up Mahout with Hadoop's fully-distributed mode
Troubleshooting Hadoop
Optimization tips
Summary
6. Visualization Chevron down icon Chevron up icon
6. Visualization
The significance of visualization in machine learning
D3.js
A visualization example for K-Means clustering
Summary
Index Chevron down icon Chevron up icon
Index

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(3 Ratings)
5 star 33.3%
4 star 0%
3 star 66.7%
2 star 0%
1 star 0%
Thimira Amaratunga Aug 17, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book provides a great starting point for everyone wanting to get in to Apache Mahout and interested in machine learning alike. I really like the way the author has explained many of the machine learning concepts without over-complicating them, which makes this a good book for any machine learning enthusiast. The step-by-step explanations make anyone try it out hands on to learn.
Amazon Verified review Amazon
Client d'Amazon Sep 08, 2016
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Medium book! Not very detailed!
Amazon Verified review Amazon
Pablo Torre Rodriguez Jul 23, 2015
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
I purchased this book and I think is a good book for those guys want to start to learn about Apache Mahout. This book provides some interesting sample code about Clustering and Recommendations. But if you want to specialize in Apache Mahout you should read Mahout in Action. For me is the best book that you can purchase about Mahout.
Amazon Verified review Amazon

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About the author

Profile icon Jayani Withanawasam
Jayani Withanawasam
Jayani Withanawasam is R&D engineer and a senior software engineer at Zaizi Asia, where she focuses on applying machine learning techniques to provide smart content management solutions. She is currently pursuing an MSc degree in artificial intelligence at the University of Moratuwa, Sri Lanka, and has completed her BE in software engineering (with first class honors) from the University of Westminster, UK. She has more than 6 years of industry experience, and she has worked in areas such as machine learning, natural language processing, and semantic web technologies during her tenure. She is passionate about working with semantic technologies and big data.
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