Key Features
Book 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
Who this book is for
Table of Contents
- Understanding Clustering
- Understanding K-Means Clustering
- Understanding Canopy clustering using Mahout
- Understanding Fuzzy K-Means Algorithm using Mahout
- Understanding Model based Clustering
- Understanding Streaming KMeans Algorithm
- Understanding Spectral Clustering
- Improving Cluster Quality
- Creating Cluster model for production
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