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  • Book Overview & Buying Apache Mahout Essentials
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Apache Mahout Essentials

Apache Mahout Essentials

By : Jayani Withanawasam
3.7 (3)
Buy this Book
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Apache Mahout Essentials

Apache Mahout Essentials

3.7 (3)
By: Jayani Withanawasam
Buy this Book

Overview of this book

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.
Table of Contents (8 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Introducing Apache Mahout
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1. Introducing Apache Mahout
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Machine learning in a nutshell
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Machine learning applications
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Machine learning libraries
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The story so far
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Apache Mahout
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How Apache Mahout works?
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From Hadoop MapReduce to Spark
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When is it appropriate to use Apache Mahout?
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Summary
2
2. Clustering
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2. Clustering
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Unsupervised learning and clustering
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Applications of clustering
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Types of clustering
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K-Means clustering
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Distance measure
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K-Means clustering with MapReduce
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Additional clustering algorithms
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Text clustering
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Optimizing clustering performance
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Summary
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3. Regression and Classification
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3. Regression and Classification
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Supervised learning
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Predictive analytics' techniques
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Classification versus regression
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Linear regression with Apache Spark
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Logistic regression with SGD
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The Naïve Bayes algorithm
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Hidden Markov Model
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Summary
4
4. Recommendations
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4. Recommendations
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Collaborative versus content-based filtering
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User-based recommenders
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Item-based recommenders
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Matrix factorization-based recommenders
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Singular value decomposition
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Summary
5
5. Apache Mahout in Production
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5. Apache Mahout in Production
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Introduction
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Apache Mahout with Hadoop
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Setting up Hadoop
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Monitoring Hadoop
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Setting up Mahout with Hadoop's fully-distributed mode
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Troubleshooting Hadoop
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Optimization tips
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Summary
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6. Visualization
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6. Visualization
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The significance of visualization in machine learning
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D3.js
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A visualization example for K-Means clustering
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Summary
7
Index
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Index

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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Apache Mahout Essentials
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