R Data Science Essentials by Sharan Kumar Ravindran & Raja B. Koushik

Author:Sharan Kumar Ravindran & Raja B. Koushik [Sharan Kumar Ravindran]
Language: eng
Format: epub
Tags: Data Visualization
Publisher: Packt Publishing
Published: 2016-01-12T22:00:00+00:00

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 in the medical field, matching the DNA to a suitable group, grouping the similar customers for the marketing campaigns, grouping the students based on their similarity in academics, and in various fields of research.

There are different methods of clustering based on the centroid, connectivity, distribution, and density. In this chapter, we will cover some of the clustering algorithms and their implementation using R. We will also cover some of the business use cases that can be solved using the clustering algorithms.

The topics that will be covered in this chapter are as follows:

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