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

Fuzzy C-means clustering


In Chapter 3, Learning from Big Data, we saw the k-means clustering algorithm, which is an iterative unsupervised algorithm that creates k clusters for a dataset based on the distance from a random centroid in the first iteration step. The centriods are calculated in each iteration to accommodate new data points. This process is repeated until the centriods do not change significantly after a point. As a result of the k-means clustering algorithm, we get discrete clusters with data points. Each data point either belongs to a cluster or it does not. There are only two states for a data point in terms of cluster membership. However, in real-world scenarios, we have data points that may belong to multiple clusters with different degrees of membership. The algorithms that create fuzzy membership instead of crisp membership for the data points within a cluster are termed soft-clustering algorithms. C-means clustering is one of the most popular algorithms, which is iterative...