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Rapid - Apache Mahout Clustering designs
Rapid - Apache Mahout Clustering designs

Rapid - Apache Mahout Clustering designs: Explore clustering algorithms used with Apache Mahout

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Rapid - Apache Mahout Clustering designs

Chapter 2. Understanding K-means Clustering

In the previous chapter, we discussed clustering and the different types of clustering in general. In this chapter, we will discuss one of the most popular algorithms in clustering, K-means. We will discuss the following topics in this chapter:

  • Learning K-means
  • Using Mahout to execute K-means
  • Visualizing the K-means cluster using Mahout

Learning K-means

As you cannot do engineering without math, in the same way, you cannot start a clustering discussion without K-means. This is one of the basic and most useful algorithms.

The name of the algorithm is K-means because by using this, we divide the set of data into K-different clusters. So, this algorithm puts a hard limitation on the number of clusters formed. K-means algorithms follow these steps:

  1. The algorithm will start with the selection of the number of clusters—K.
  2. It will initialize the K centroid points in the cluster.
  3. Now, the closest points of each centroid are computed.
    Learning K-means
  4. Next, the centroid location is recomputed for each cluster.
    Learning K-means
  5. Steps 3 and 4 are repeated until the convergence is reached.

Convergence is reached when the location of centroids does not move from one iteration to the next. In an algorithm, we also provide a convergence threshold, which indicates that the centroid does not move more than this distance, and if it is reached, we stop the algorithm.

The...

Visualizing clusters

The T Mahout example package provides classes to generate a sample dataset.

For K-means, DisplayKmeans is the class that displays the cluster. You can directly run the class. As per the code in the class, points are generated as follows:

generateSamples(500, 1, 1, 3); // 500 samples of sd 3
generateSamples(300, 1, 0, 0.5); //300 sample of sd 0.5
generateSamples(300, 0, 2, 0.1); //300 sample of sd 0.1

Data is a set of randomly-generated 2D data points, and the points are generated using a normal distribution centered at a mean location with a constant standard deviation.

Once you run this class, you will view the clusters, as shown here:

Visualizing clusters

The final clustering done by the algorithm is shown using a bold red colored circle. In the console, you can find the output related to points generation and cluster formation.

Visualizing clusters

Summary

We discussed K-Clustering in this chapter. We also discussed how the K-means algorithm works and we used the Mahout implementation of K-means on a text dataset. We downloaded the data and converted it to a Mahout reusable vector format.

We discussed how to understand the cluster using the clusterdumper utility. We saw an example class to visualize the Mahout cluster as given in the Mahout example class.

Now, we will move on to the next chapter, where we will discuss Canopy clustering. This is also a very good technique and can be used to estimate the number of K for K-means clustering.

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Description

As more and more organizations are discovering the use of big data analytics, interest in platforms that provide storage, computation, and analytic capabilities has increased. Apache Mahout caters to this need and paves the way for the implementation of complex algorithms in the field of machine learning to better analyse your data and get useful insights into it. Starting with the introduction of clustering algorithms, this book provides an insight into Apache Mahout and different algorithms it uses for clustering data. It provides a general introduction of the algorithms, such as K-Means, Fuzzy K-Means, StreamingKMeans, and how to use Mahout to cluster your data using a particular algorithm. You will study the different types of clustering and learn how to use Apache Mahout with real world data sets to implement and evaluate your clusters. This book will discuss about cluster improvement and visualization using Mahout APIs and also explore model-based clustering and topic modelling using Dirichlet process. Finally, you will learn how to build and deploy a model for production use.

What you will learn

  • Explore clustering algorithms and cluster evaluation techniques
  • Learn different types of clustering and distance measuring techniques
  • Perform clustering on your data using KMeans clustering
  • Discover how canopy clustering is used as preprocess step for KMeans
  • Use the Fuzzy KMeans algorithm in Apache Mahout
  • Implement Streaming KMeans clustering in Mahout
  • Learn Spectral KMeans clustering implementation of Mahout

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Publication date : Oct 08, 2015
Length: 130 pages
Edition : 1st
Language : English
ISBN-13 : 9781783284443
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Publication date : Oct 08, 2015
Length: 130 pages
Edition : 1st
Language : English
ISBN-13 : 9781783284443
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Concepts :
Tools :

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Table of Contents

10 Chapters
1. Understanding Clustering Chevron down icon Chevron up icon
2. Understanding K-means Clustering Chevron down icon Chevron up icon
3. Understanding Canopy Clustering Chevron down icon Chevron up icon
4. Understanding the Fuzzy K-means Algorithm Using Mahout Chevron down icon Chevron up icon
5. Understanding Model-based Clustering Chevron down icon Chevron up icon
6. Understanding Streaming K-means Chevron down icon Chevron up icon
7. Spectral Clustering Chevron down icon Chevron up icon
8. Improving Cluster Quality Chevron down icon Chevron up icon
9. Creating a Cluster Model for Production Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Vishnu Nov 16, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book to learn about classifications and how to implement classification using mahout.
Amazon Verified review Amazon
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