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MATLAB for Machine Learning

You're reading from   MATLAB for Machine Learning Practical examples of regression, clustering and neural networks

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781788398435
Length 382 pages
Edition 1st Edition
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Authors (2):
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Pavan Kumar Kolluru Pavan Kumar Kolluru
Author Profile Icon Pavan Kumar Kolluru
Pavan Kumar Kolluru
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (10) Chapters Close

Preface 1. Getting Started with MATLAB Machine Learning FREE CHAPTER 2. Importing and Organizing Data in MATLAB 3. From Data to Knowledge Discovery 4. Finding Relationships between Variables - Regression Techniques 5. Pattern Recognition through Classification Algorithms 6. Identifying Groups of Data Using Clustering Methods 7. Simulation of Human Thinking - Artificial Neural Networks 8. Improving the Performance of the Machine Learning Model - Dimensionality Reduction 9. Machine Learning in Practice

Hierarchical clustering

In MATLAB, hierarchical clustering produces a cluster tree or dendrogram by grouping data. A multilevel hierarchy is created, where clusters at one level are joined as clusters at the next level. From individual statistical units, the most closely related statistical units are aggregated at each iteration. In the Statistics and Machine Learning Toolbox, there is everything you need to do agglomerative hierarchical clustering. Using the pdist, linkage, and cluster functions, the clusterdata function performs agglomerative clustering. Finally, the dendrogram function plots the cluster tree.

As said, the procedure for forming the dendrogram requires the use of multiple functions. These functions are called by the clusterdata function, which represents the main function.

Analyzing the sequence of calls of these functions in detail can be particularly useful...

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