Introduction to Clustering

Imagine you have a large collection of items and want to organize them into meaningful groups. For example, think about books in a library. Libraries organize books into categories like fiction, science, history, and more. This makes it easier to find the books you're interested in — and that's what clustering is all about.

In essence, clustering is about:

Grouping similar data points together: data points within the same cluster are more similar to each other than to those in other clusters;
Uncovering hidden structures: clustering can reveal underlying patterns and organization in data that might not be immediately obvious;
Making sense of complex data: by grouping data, clustering simplifies large datasets and helps us understand them better.

Clustering is used in many different fields and for a wide variety of purposes.

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Sektion 1. Kapitel 1

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Kursusindhold

Cluster Analysis

1. Clustering Fundamentals

Introduction to Clustering Clustering Vs Classification Clustering Algorithms and Libraries

2. Core Concepts

Missing Values Handling Categorical Features Encoding Data Normalization Distance Measures Linkages Challenge: Preprocessing the Dataset

3. K-Means

4. Hierarchical Clustering

How Hierarchical Clustering Works?Optimal Number of Clusters Implementing on Dummy Dataset Implementing on Customers Dataset Challenge: Implementing Hierarchical Clustering

5. DBSCAN

Why DBSCAN?How DBSCAN Works?How to Assign Points to the Clusters?Implementing on Dummy Dataset Implementing on Real Dataset Challenge: Implementing DBSCAN

6. GMMs