Book Image

Artificial Intelligence for Big Data

By : Anand Deshpande, Manish Kumar
Book Image

Artificial Intelligence for Big Data

By: Anand Deshpande, Manish Kumar

Overview of this book

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Data clustering


So far, we have primarily explored supervised learning methods where we have a historical trail of data that is used for training the machine learning models. However, there is a very common scenario where the machine needs to classify objects or entities into various groups based on predefined or runtime categories. For example, in the dataset that contains information about employees, we need to categorize the employees based on one or more attributes combined. With this, the goal is to group similar objects and partition the data based on similarities.

The general idea is to have a consistent attribute map within a group and distinct behaviors across the groups. Unlike the supervised learning methods, there are no dependent variables in the case of data clustering. A cluster represents various groups of entities that demonstrate similarities in attributes. At a broader level, clustering has two types:

  • Fixed clustering: In this type of clustering, each of the data points...