Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying R Data Science Essentials
  • Table Of Contents Toc
R Data Science Essentials

R Data Science Essentials

By : Koushik, Kumar Ravindran
3 (3)
close
close
R Data Science Essentials

R Data Science Essentials

3 (3)
By: Koushik, Kumar Ravindran

Overview of this book

With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.
Table of Contents (10 chapters)
close
close
9
Index

Chapter 4. Segmentation Using Clustering

Clustering is often considered a classic example of unsupervised learning. It is a method of dividing the dataset into multiple groups where the objects in the same group will be more similar to each other than those in the other groups.

Clustering algorithms are generally used on unlabeled datasets; hence, there is no way to measure the clustering output. The user, based on his requirement, should consider the variables carefully so that the resultant clusters closely match with the user's requirement.

The greatest example for the clustering algorithms would be a search engine where the pages that are closely related to each other are shown together and the pages that are different are kept apart as far as possible. The most important factor here is to measure the similarity or dissimilarity between the objects.

Some of the problems that can be solved through the implementation of clustering algorithms are the predicting of a disease...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
R Data Science Essentials
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon