Conclusion

The Gaussian mixture model is a versatile clustering algorithm that addresses the limitations of methods like K-means by handling overlapping clusters and complex data distributions. Throughout this section, you saw its effectiveness on both synthetic and real-world datasets.

In summary, GMM provides a more robust solution for clustering tasks involving overlapping and non-spherical clusters, making it ideal for more complex datasets.

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Sektion 6. Kapitel 7

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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

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

Conclusion

In summary, GMM provides a more robust solution for clustering tasks involving overlapping and non-spherical clusters, making it ideal for more complex datasets.

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Sektion 6. Kapitel 7