Module 5: Unsupervised Learning & Dimensionality Reduction

This section introduces unsupervised learning, focusing on clustering techniques such as K-Means, Hierarchical Clustering, and DBSCAN, emphasizing their applications and importance.

Week 9: Clustering Techniques

This section introduces clustering techniques within unsupervised learning, focusing on K-Means, Hierarchical Clustering, and DBSCAN.

Introduction To Unsupervised Learning

Unsupervised learning focuses on analyzing unlabeled data to uncover hidden patterns and structures within datasets without predefined labels.

Key Tasks Within Unsupervised Learning

Unsupervised learning focuses on discovering hidden patterns in unlabeled data, primarily through clustering, dimensionality reduction, and association rule mining.

K-Means Clustering

K-Means clustering is a popular unsupervised learning algorithm that partitions data into K distinct clusters based on proximity to cluster centroids.

K-Means Algorithm: A Step-By-Step Iterative Process

The K-Means algorithm iteratively partitions observations into clusters, refining cluster assignments until convergence is achieved through a series of steps including initialization, assignment, and update phases.

K Selection: Determining The Optimal Number Of Clusters (K)

This section explains the importance of selecting the optimal number of clusters (K) in K-Means clustering and discusses two popular methods: the Elbow Method and Silhouette Analysis.

Hierarchical Clustering

Hierarchical clustering is an unsupervised learning technique that builds a tree-like structure of clusters without pre-specifying the number of clusters.

Agglomerative Hierarchical Clustering (Bottom-Up Approach)

Agglomerative Hierarchical Clustering is a bottom-up approach that merges individual data points into larger clusters without the need for pre-specifying the number of clusters.

Dendrograms: Visualizing The Cluster Hierarchy

Dendrograms are tree-like diagrams used in hierarchical clustering to visualize the merging of clusters based on their similarities or dissimilarities.

Dbscan (Density-Based Spatial Clustering Of Applications With Noise)

DBSCAN is a powerful density-based clustering algorithm that identifies clusters of arbitrary shapes and detects outliers as noise.

Lab: Applying And Comparing Different Clustering Algorithms, Interpreting Their Results

This lab focuses on practical applications and comparisons of various clustering algorithms, including K-Means, Hierarchical Clustering, and DBSCAN, amidst hands-on data analysis.

Lab Objectives

This section outlines the objectives of the lab session focused on unsupervised learning and clustering techniques.

Prepare Data For Clustering With Precision

This section discusses the importance of data preparation for clustering, highlighting techniques such as exploratory data analysis, handling missing values, and feature scaling to ensure effective clustering results.

Implement K-Means Clustering With Optimal K Selection

This section covers the implementation of K-Means clustering and methods for selecting the optimal number of clusters, K.

Implement Hierarchical Clustering With Dendrogram Interpretation

This section focuses on Hierarchical Clustering, specifically the agglomerative approach and the interpretation of dendrograms.

Implement Dbscan (Density-Based Spatial Clustering Of Applications With Noise)

DBSCAN is a density-based clustering algorithm that can identify clusters of arbitrary shapes and detect outliers.

Comprehensive Performance Comparison And In-Depth Discussion

This section provides a detailed comparison of clustering algorithms focusing on K-Means, Hierarchical Clustering, and DBSCAN, evaluating their performance, strengths, and weaknesses.

Expected Outcomes

This section outlines the expected outcomes of mastering clustering algorithms in the realm of unsupervised learning.