Book Image

Big Data Analytics with Java

Book Image

Big Data Analytics with Java


Overview of this book

This book covers case studies such as sentiment analysis on a tweet dataset, recommendations on a movielens dataset, customer segmentation on an ecommerce dataset, and graph analysis on actual flights dataset. This book is an end-to-end guide to implement analytics on big data with Java. Java is the de facto language for major big data environments, including Hadoop. This book will teach you how to perform analytics on big data with production-friendly Java. This book basically divided into two sections. The first part is an introduction that will help the readers get acquainted with big data environments, whereas the second part will contain a hardcore discussion on all the concepts in analytics on big data. It will take you from data analysis and data visualization to the core concepts and advantages of machine learning, real-life usage of regression and classification using Naïve Bayes, a deep discussion on the concepts of clustering,and a review of simple neural networks on big data using deepLearning4j or plain Java Spark code. This book is a must-have book for Java developers who want to start learning big data analytics and want to use it in the real world.
Table of Contents (21 chapters)
Big Data Analytics with Java
About the Author
About the Reviewers
Customer Feedback
Free Chapter
Big Data Analytics with Java
Ensembling on Big Data
Real-Time Analytics on Big Data

Chapter 10. Clustering and Customer Segmentation on Big Data

Up until now we have only used and worked on data that was prelabeled that is, supervised. Based on that prelabeled data, we trained our machine learning models and predicted our results. But what if the data is not labeled at all and we just get plain data? In that case, can we carry out any useful analysis of the data at all? Figuring out details from an unlabeled dataset is an example of unsupervised learning, where the machine learning algorithm makes deductions or predictions from raw unlabeled data. One of the most popular approaches to analyzing this unlabeled data is to find groups of similar items within a dataset. This grouping of data has several advantages and use cases, as we will see in this chapter.

In this chapter, we will cover the following topics:

  • The concepts of clustering and types of clustering, including k-means and bisecting k-means clustering

  • Advantages and use cases of clustering

  • Customer segmentation and...