Apache Mahout Clustering Designs
āĻāĻ āĻ-āĻŦā§āĻā§āĻ° āĻŦāĻŋāĻˇā§ā§
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 LearnExplore clustering algorithms and cluster evaluation techniquesLearn different types of clustering and distance measuring techniquesPerform clustering on your data using K-Means clusteringDiscover how canopy clustering is used as pre-process step for K-MeansUse the Fuzzy K-Means algorithm in Apache MahoutImplement Streaming K-Means clustering in MahoutLearn Spectral K-Means clustering implementation of MahoutIn DetailAs 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 approachThis 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.
āĻ˛ā§āĻāĻ āĻ¸āĻŽā§āĻĒāĻ°ā§āĻā§
Ashish Gupta has been working in the field of software development for the last 10 years. He has worked in companies such as SAP Labs and Caterpillar as a software developer. While working for a start-up predicting potential customers for new fashion apparels using social media, he developed an interest in the field of machine learning. Since then, he has worked on big data technologies and machine learning for different industries, including retail, finance, insurance, and so on. He is passionate about learning new technologies and sharing that knowledge with others. He is the author of the book, Learning Apache Mahout Classification, Packt Publishing. He has organized many boot camps for Apache Mahout and the Hadoop ecosystem.
