What is K-Means Clustering?

Among clustering algorithms, K-means is a widely popular and effective method. It partitions data into K distinct clusters, where K is a pre-defined number.

The goal of K-means is to minimize distances within clusters and maximize distances between clusters. This creates internally similar and externally distinct groups. K-means has numerous applications, such as:

Customer segmentation: grouping customers for targeted marketing;
Document clustering: organizing documents by topic;
Image segmentation: dividing images for object recognition;
Anomaly detection: identifying unusual data points.

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Section 3. Chapter 1

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Course Content

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

Problem Statement What is Gaussian Distribution?How GMMs Work?Implementing GMM on Dummy Data Implementing GMM on Real Data Challenge: Implementing Gaussian Mixture Models Conclusion

What is K-Means Clustering?

Among clustering algorithms, K-means is a widely popular and effective method. It partitions data into K distinct clusters, where K is a pre-defined number.

Customer segmentation: grouping customers for targeted marketing;
Document clustering: organizing documents by topic;
Image segmentation: dividing images for object recognition;
Anomaly detection: identifying unusual data points.

Everything was clear?

Thanks for your feedback!

Section 3. Chapter 1