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


In this chapter, we learnt about clustering and we saw how this approach helps to group different items into groups with each group having items which are similar to them in some form. Clustering is an example of unsupervised learning and there are lots of popular clustering algorithms that are shipped by default in the Apache Spark package. We learnt about two clustering approaches, the first being k-means approach where items that are closer to each other based on some mathematical formula like Euclidean distance and so on were grouped together. We also learnt about bisecting k-means approach which is essentially and improvement on the regular k-means clustering and is creating by being a combination of hierarchical and k-means clustering. We also applied clustering on a sample dataset of retail from UCI. On this sample case study we segmented the customers of the website using clustering and tried to figure out the important customers for an online e-commerce store.

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