Cluster Analysis

Kursusindhold

Cluster Analysis

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

What is K-Means Clustering?How K-Means Algorithm Works?Finding Optimal Number of Clusters Using WSS Finding Optimal Number of Clusters Using Silhouette Score Implementing on Dummy Dataset Implementing on Real Dataset Challenge: Implementing K-Means Clustering

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

Problem Statement

Soft Clustering

Soft clustering assigns probabilities of belonging to each cluster rather than forcing each data point into just one group. This approach is especially useful when clusters overlap or when data points lie near the boundary of multiple clusters. It's widely used in applications like customer segmentation, where individuals might exhibit behaviors belonging to multiple groups at once.

Problems with K-Means and DBSCAN

Clustering algorithms like K-means and DBSCAN are powerful but have limitations:

Both algorithms face challenges with high-dimensional data and overlapping clusters. These limitations highlight the need for flexible approaches like Gaussian mixture models, which handle complex data distributions more effectively. For example, think about this type of data:

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Sektion 6. Kapitel 1

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Kursusindhold

Cluster Analysis

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

What is K-Means Clustering?How K-Means Algorithm Works?Finding Optimal Number of Clusters Using WSS Finding Optimal Number of Clusters Using Silhouette Score Implementing on Dummy Dataset Implementing on Real Dataset Challenge: Implementing K-Means Clustering

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

Problem Statement

Soft Clustering

Soft clustering assigns probabilities of belonging to each cluster rather than forcing each data point into just one group. This approach is especially useful when clusters overlap or when data points lie near the boundary of multiple clusters. It's widely used in applications like customer segmentation, where individuals might exhibit behaviors belonging to multiple groups at once.

Problems with K-Means and DBSCAN

Clustering algorithms like K-means and DBSCAN are powerful but have limitations:

Both algorithms face challenges with high-dimensional data and overlapping clusters. These limitations highlight the need for flexible approaches like Gaussian mixture models, which handle complex data distributions more effectively. For example, think about this type of data:

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Tak for dine kommentarer!

Sektion 6. Kapitel 1

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