Chapter 4

Choosing the Best Segmentation Approach

Learning Objectives

By the end of this chapter, you will be able to:

  • Tune hyperparameters (such as the number of clusters) of clustering algorithms using various methods
  • Use the mean-shift, k-mode, and k-prototype clustering techniques
  • Evaluate and fine-tune clustering

This chapter covers various clustering algorithms (apart from k-means) and explains how they can be evaluated.

Introduction

In the previous chapter, we introduced the concept of clustering, and practiced it using k-means clustering. However, several issues remained unresolved, such as how to choose the number of clusters and how to evaluate a clustering technique once the clusters are created. This chapter aims to expand ...

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