K-Medoids Clustering Implementation
├── Introduction
│   └── Overview of K-Medoids
├── Setting Up the Environment
│   ├── Importing Libraries
│   └── Generating the Dataset
├── Implementing K-Medoids
│   ├── Data Preparation
│   ├── Model Training
│   └── Identifying Medoids
├── Visualization
│   └── K-Medoids Clustering Visualization
└── Conclusion
    └── Insights and Observations

1. Introduction

Overview of K-Medoids

• K-Medoids is a clustering algorithm similar to K-Means, but instead of calculating the mean of the objects in a cluster, it selects actual objects as the representative points (medoids).

2. Setting Up the Environment

Importing Libraries

# Python code to import necessary libraries
import numpy as np
import matplotlib.pyplot as plt
from pyclustering.cluster.kmedoids import kmedoids
from pyclustering.cluster import cluster_visualizer
from pyclustering.utils import read_sample
from pyclustering.samples.definitions import SIMPLE_SAMPLES

Generating the Dataset

# Python code to generate a sample dataset
X = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE3)

3. Implementing K-Medoids

Data Preparation

• Choosing initial medoids.

initial_medoids = [1, 10]

Model Training

# Python code to train the K-Medoids model
kmedoids_instance = kmedoids(X, initial_medoids)
kmedoids_instance.process()

Identifying Medoids

# Python code to identify the medoids
medoids = kmedoids_instance.get_medoids()

4. Visualization