Computer Science & Information Management

Data Clustering Algorithms in R Anand Khandare, PhD Ordering Info/Buy Book

In Production Pub Date: Forthcoming March 2024 Hardback Price: $170 US | £130 UK Hard ISBN: 9781774916445 Pages: est. 180 pp w index Binding Type: Hardback / ebook Notes: 15 color and 91 b/w illustrations

Clustering enables partition and segmentation of data in machine learning. It is extremely important for exploratory data analysis and plays an important role in modern machine learning. This book, Data Clustering Algorithms in R, provides a practical guide to clustering algorithms using R programming, a programming language for statistical computing and graphics. The volume provides readers with simple and practical steps to understand and implement the various procedures, from installing the basic requirements of the R language to properly analyzing data sets and inferring conclusions based on the plots.

The book begins with the basics of installation, right from finding the links to the sites for executable files to installing the language parameters into their respective systems. It then progresses to the basics of R language and then to clustering and optimization. The volume explains the various algorithms along with their differences and applications, providing an overview of the essential concepts one needs to learn and keep in mind for effective analysis of data. The various algorithmic techniques explained include k-means clustering, k-medoids agglomerative and divisive clustering, density-based spatial clustering of applications with noise (DBSCAN), model-based clustering, and simulation and comparison of clustering, as well as improved clustering approaches.

The book is arranged to progressively increase its complexity to allow the reader to understand the process easily without ambiguity.

Key features:

• Written in language simple enough for even beginners to understand
• Provides practical steps for the effective analysis of data
• Explains the various clustering algorithms and when to use them
• Gives guidelines on how to properly analyze data sets

CONTENTS:
Preface

1. Introduction to R Programming
1. 1. Overview of R
1.2. Installing R and R-Studio
1.3. Using R and R- Studio
1.4. Installing and loading R packages
1.5. Using data sets in R
1.6. Graphs and Charts in R
1.7 Summary

2. Programming Fundamental with R
2.1. Data Types, Variable and Operators
2.2. Conditional Control Structures
2.3. Loop Control Structures
2.4. Function in R
2.5. Data Structures
2.6 Summary

3. Introduction to Clustering
3.1 Overview
3.2. Types of Clustering
3.3. Clustering Applications
3.4. Requirements of Clustering
3.5. Distance and Similarity Measures
3.6. Cluster Validation
3.7. Finding Optimal Number of Clusters
3.8. Summary

4. K-Means Clustering
4.1. Basic idea
4.2. Algorithm
4.3. Implementation in R
4.4. Applications
4.5. Advantages and Disadvantages
4.6. Summary

5. K-Medoids Clustering
5.1. Basic idea
5.2. Algorithm
5.3. Implementation in R
5.4. Applications
5.5. Advantages and Disadvantages
5.6. Summary

6. Agglomerative Clustering and Divisive Clustering
6.1. Basic idea
6.2. Algorithm
6.3. Implementation in R
6.4. Applications
6.5. Advantages and Disadvantages
6.6. Summary

7. DBSCAN Clustering
7.1. Basic idea
7.2. Algorithm
7.3. Implementation in R
7.4. Applications
7.5. Advantages and Disadvantages
7.6. Summary

8. Model-Based Clustering
8.1. Basic idea
8.2. Algorithm
8.3. Implementation in R
8.4. Applications
8.5. Advantages and Disadvantages
8.6. Summary

9. Simulation and Comparison of Clustering
9.1. Simulation on Small Data
9.2. Simulation on Big Data
9.3. Comparative Study
9.4. Summary

10. Improved Clustering
10.1 Overview
10.2. Study of Improved Clustering
10.3. Simulation
10.4. Comparative Study
10.5. Summary

Index

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