Introduction

Brief Explanation of the Tutorial's Goal

This tutorial aims to introduce the concept of clustering, a technique used in unssupervised learning. We will primarily focus on K-Means and Hierarchical Clustering techniques.

What the User Will Learn

By the end of this tutorial, the user will get a comprehensive understanding of clustering techniques and be able to implement K-Means and Hierarchical clustering using Python programming.

Prerequisites

The user should have a basic understanding of Python programming and knowledge of machine learning concepts.

Step-by-Step Guide

Detailed Explanation of Concepts

Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. In simple words, the aim is to segregate groups with similar traits and assign them into clusters.

There are two types of clustering we'll focus on:
1. K-Means Clustering: K-means is a centroid-based algorithm, or a distance-based algorithm, where we calculate the distances to assign a point to a cluster. In K-Means, each cluster is associated with a centroid.
2. Hierarchical Clustering: Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters.

Clear Examples with Comments

In K-Means clustering, we initialize 'k' centroids randomly, then assign each data point to the nearest centroid, and find the new centroid by taking the average of all points in the cluster. The steps are repeated until the centroid does not change.

In Hierarchical Clustering, we start by treating each data point as a cluster. Then, we merge the two closest clusters together on the basis of some distance measure. This process continues until only a single cluster is left.

Best Practices and Tips

1. Always normalize your data before applying any clustering technique.
2. Choose the right number of clusters in K-Means clustering.
3. Visualize your data before and after applying clustering.

Code Examples

K-Means Clustering

# Importing Required Libraries
from sklearn.cluster import KMeans
import pandas as pd

# Creating a Dataframe
data = pd.DataFrame({
    'x': [12, 20, 28, 18, 29, 33, 24, 45, 45, 52, 51, 52, 55, 53, 55, 61, 64, 69, 72],
    'y': [39, 36, 30, 52, 54, 46, 55, 59, 63, 70, 66, 63, 58, 23, 14, 8, 19, 7, 24]
})

# Initializing KMeans
kmeans = KMeans(n_clusters=3)  # Number of clusters

# Fitting with inputs
kmeans = kmeans.fit(data)

# Predicting the clusters
labels = kmeans.predict(data)  # Gives you the cluster number for each data point

# Getting the cluster centers
C = kmeans.cluster_centers_  # Gives you the cluster centroids

Hierarchical Clustering

# Importing Required Libraries
from scipy.cluster.hierarchy import dendrogram, linkage
import matplotlib.pyplot as plt

# Creating a Dataframe
data = pd.DataFrame({
    'x': [12, 20, 28, 18, 29, 33, 24, 45, 45, 52, 51, 52, 55, 53, 55, 61, 64, 69, 72],
    'y': [39, 36, 30, 52, 54, 46, 55, 59, 63, 70, 66, 63, 58, 23, 14, 8, 19, 7, 24]
})

# Creating a Linkage Matrix
linked = linkage(data, 'single')

# Dendrogram
dendrogram(linked,  
            orientation='top',
            labels=data.index,
            distance_sort='descending',
            show_leaf_counts=True)
plt.show()

Summary

In this tutorial, we covered the basics of clustering techniques in unsupervised learning, focusing on K-Means and Hierarchical Clustering. We went through a step-by-step guide on how to implement these techniques and provided code snippets for better understanding.

Practice Exercises

1. Implement K-Means clustering on the Iris dataset and visualize the clusters.
2. Implement Hierarchical clustering on the same Iris dataset and compare the results with K-Means clustering.

Next Steps for Learning

Continue learning more advanced clustering techniques like DBSCAN, Mean-Shift etc. Also, study about the ways to determine the optimal number of clusters like Elbow Method, Silhouette Method etc.

Additional Resources

1. Scikit-Learn Documentation
2. Python Data Science Handbook by Jake VanderPlas
3. Machine Learning by Andrew Ng on Coursera.