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Clusteranalyse
course content

Kursinhalt

Clusteranalyse

Clusteranalyse

1. Clustering Fundamentals
Introduction to ClusteringClustering Vs ClassificationClustering Algorithms and Libraries
2. Core Concepts
Missing Values HandlingCategorical Features EncodingData NormalizationDistance MeasuresLinkagesChallenge: Preprocessing the Dataset
3. K-Means
What is K-Means Clustering?How K-Means Algorithm Works?Finding Optimal Number of Clusters Using WSSFinding Optimal Number of Clusters Using Silhouette ScoreImplementing on Dummy DatasetImplementing on Real DatasetChallenge: Implementing K-Means Clustering
4. Hierarchical Clustering
How Hierarchical Clustering Works?Optimal Number of ClustersImplementing on Dummy DatasetImplementing on Customers DatasetChallenge: Implementing Hierarchical Clustering
5. DBSCAN
Why DBSCAN?How DBSCAN Works?How to Assign Points to the Clusters?Implementing on Dummy DatasetImplementing on Real DatasetChallenge: Implementing DBSCAN
6. GMMs
Problem StatementWhat is Gaussian Distribution?How GMMs Work?Implementing GMM on Dummy DataImplementing GMM on Real DataChallenge: Implementing Gaussian Mixture ModelsConclusion

book
Why DBSCAN?

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) offers a powerful alternative to traditional clustering algorithms like K-means and hierarchical clustering, especially when dealing with clusters of arbitrary shapes and datasets containing noise.

The table above highlights the key advantages of DBSCAN: its ability to find clusters of any shape, its robustness to noise, and its automatic determination of the number of clusters.

Therefore, DBSCAN is particularly well-suited for scenarios where:

  • Clusters have irregular shapes;

  • Noise points are present and need to be identified;

  • The number of clusters is not known beforehand;

  • Data density varies across the dataset.

question mark

In which scenario is DBSCAN likely to outperform K-means and hierarchical clustering?

Select the correct answer

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Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 5. Kapitel 1

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course content

Kursinhalt

Clusteranalyse

Clusteranalyse

1. Clustering Fundamentals
Introduction to ClusteringClustering Vs ClassificationClustering Algorithms and Libraries
2. Core Concepts
Missing Values HandlingCategorical Features EncodingData NormalizationDistance MeasuresLinkagesChallenge: Preprocessing the Dataset
3. K-Means
What is K-Means Clustering?How K-Means Algorithm Works?Finding Optimal Number of Clusters Using WSSFinding Optimal Number of Clusters Using Silhouette ScoreImplementing on Dummy DatasetImplementing on Real DatasetChallenge: Implementing K-Means Clustering
4. Hierarchical Clustering
How Hierarchical Clustering Works?Optimal Number of ClustersImplementing on Dummy DatasetImplementing on Customers DatasetChallenge: Implementing Hierarchical Clustering
5. DBSCAN
Why DBSCAN?How DBSCAN Works?How to Assign Points to the Clusters?Implementing on Dummy DatasetImplementing on Real DatasetChallenge: Implementing DBSCAN
6. GMMs
Problem StatementWhat is Gaussian Distribution?How GMMs Work?Implementing GMM on Dummy DataImplementing GMM on Real DataChallenge: Implementing Gaussian Mixture ModelsConclusion

book
Why DBSCAN?

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) offers a powerful alternative to traditional clustering algorithms like K-means and hierarchical clustering, especially when dealing with clusters of arbitrary shapes and datasets containing noise.

The table above highlights the key advantages of DBSCAN: its ability to find clusters of any shape, its robustness to noise, and its automatic determination of the number of clusters.

Therefore, DBSCAN is particularly well-suited for scenarios where:

  • Clusters have irregular shapes;

  • Noise points are present and need to be identified;

  • The number of clusters is not known beforehand;

  • Data density varies across the dataset.

question mark

In which scenario is DBSCAN likely to outperform K-means and hierarchical clustering?

Select the correct answer

War alles klar?

Wie können wir es verbessern?

Danke für Ihr Feedback!

Abschnitt 5. Kapitel 1
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