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

The K-means algorithm


K-means is one of the most popular unsupervised algorithms for data clustering, which is used when we have unlabeled data without defined categories or groups. The number of clusters is represented by the k variable. This is an iterative algorithm that assigns the data points to a specific cluster based on the distance from the arbitrary centroid. During the first iteration, the centroids are randomly defined and the data points are assigned to the cluster based on the least vicinity from the centroid. Once the data points are allocated, within the subsequent iterations, the centroids are realigned to the mean of the data points and the data points are once again added to the clusters based on the least vicinity from the centroids. These steps are iterated to the point where the centroids do not change more than the set threshold. Let's illustrate the K-means algorithm with three iterations on a sample two dimensional (x1, x2) dataset:

Iteration 1:

  1. During the first iteration...