Explore clustering algorithms used with Apache Mahout
About This Book
- Use Mahout for clustering datasets and gain useful insights
- Explore the different clustering algorithms used in day-to-day work
- A practical guide to create and evaluate your own clustering models using real world data sets
Who This Book Is For
This book is for developers who want to try out clustering on large datasets using Mahout. It will also be useful for those users who don't have background in Mahout, but have knowledge of basic programming and are familiar with basics of machine learning and clustering. It will be helpful if you know about clustering techniques with some other tool.
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 K-Means clustering
- Discover how canopy clustering is used as pre-process step for K-Means
- Use the Fuzzy K-Means algorithm in Apache Mahout
- Implement Streaming K-Means clustering in Mahout
- Learn Spectral K-Means clustering implementation of Mahout
In Detail
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.
Style and approach
This book is a hand's-on guide with examples using real-world datasets. Each chapter begins by explaining the algorithm in detail and follows up with showing how to use mahout for that algorithm using example data-sets.
