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Rapid - Apache Mahout Clustering designs

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Rapid - Apache Mahout Clustering designs

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Rapid - Apache Mahout Clustering designs
Oct, 2015 | 130 pages
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Table of Contents (16 chapters)
Apache Mahout Clustering Designs
Apache Mahout Clustering Designs
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Understanding Clustering
Understanding Clustering
The clustering concept
Understanding distance measures
Understanding different clustering techniques
Algorithm support in Mahout
Clustering algorithms in Mahout
Installing Mahout
Preparing data for use with clustering techniques
Summary
2
Understanding K-means Clustering
Understanding K-means Clustering
Learning K-means
Visualizing clusters
Summary
3
Understanding Canopy Clustering
Understanding Canopy Clustering
Running Canopy clustering on Mahout
Visualizing clusters
Working with CSV files
Summary
4
Understanding the Fuzzy K-means Algorithm Using Mahout
Understanding the Fuzzy K-means Algorithm Using Mahout
Learning Fuzzy K-means clustering
Visualizing clusters
Summary
5
Understanding Model-based Clustering
Understanding Model-based Clustering
Learning model-based clustering
Running LDA using Mahout
Summary
6
Understanding Streaming K-means
Understanding Streaming K-means
Learning Streaming K-means
Using Mahout for streaming K-means
Summary
7
Spectral Clustering
Spectral Clustering
Understanding spectral clustering
Mahout implementation of spectral clustering
Summary
8
Improving Cluster Quality
Improving Cluster Quality
Evaluating clusters
Using DistanceMeasure interface
Summary
9
Creating a Cluster Model for Production
Creating a Cluster Model for Production
Preparing the dataset
Launching the Mahout job on the cluster
Performance tuning for the job
Summary
Index
Index

Chapter 4. Understanding the Fuzzy K-means Algorithm Using Mahout

In the previous chapter, we discussed Canopy clustering and used Mahout to run Canopy clustering. In this chapter, we will discuss a new algorithm in clustering—Fuzzy K-means. Fuzzy clustering is also called soft clustering because data points can belongs to more than one cluster. Fuzzy K-means clustering is also called Fuzzy C-Means (FCM). We will discuss the following topics in this chapter:

  • Learning Fuzzy K-means clustering

  • Using mahout to execute Fuzzy K-means clustering

  • Visualizing the Fuzzy K-means cluster using Mahout

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