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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
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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
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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
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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
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Index
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Index

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...

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Apache Mahout Essentials
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