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Machine Learning and Deep Learning

337What is Clustering?
338The K-Means Algorithm
339K-Means Objective Function
340Initialization Methods
341Choosing K: The Elbow Method
342Silhouette Score
343K-Means Limitations
344Distance Metrics in K-Means
345Feature Scaling for K-Means
346Implementing K-Means with Scikit-learn
347Limitations of K-Means and Motivation for Density-Based Methods
348DBSCAN: Core Concepts and Definitions
349DBSCAN Algorithm Step-by-Step
350Choosing Epsilon and MinPts Parameters
351DBSCAN Advantages and Handling Arbitrary Shapes
352DBSCAN Limitations and Failure Cases
353HDBSCAN: Hierarchical Density-Based Clustering
354Implementing and Evaluating Density-Based Clustering
355Hierarchical Clustering Overview
356Dendrograms and Tree Representations
357Linkage Criteria: Single, Complete, and Average
358Ward's Linkage and Variance Minimization
359Distance Metrics for Hierarchical Clustering
360Agglomerative Clustering Algorithm
361Computational Complexity and Scalability
362Implementing Hierarchical Clustering with Scikit-learn
363From K-Means to Probabilistic Clustering
364Gaussian Distribution as Cluster Model
365Mixture Model Definition
366Likelihood Function for GMMs
367The Expectation-Maximization Algorithm
368E-Step: Computing Responsibilities
369M-Step: Updating Parameters
370Model Selection: Choosing Number of Components
371Covariance Structure Constraints
372GMM Implementation and Applications
373What is Anomaly Detection?
374Statistical Approaches to Anomaly Detection
375Density-Based Anomaly Detection
376Isolation Forest Algorithm
377One-Class SVM for Novelty Detection
378Autoencoders for Anomaly Detection
379Evaluation Metrics for Anomaly Detection
380Anomaly Detection in Practice

Machine Learning and Deep Learning

337What is Clustering?
338The K-Means Algorithm
339K-Means Objective Function
340Initialization Methods
341Choosing K: The Elbow Method
342Silhouette Score
343K-Means Limitations
344Distance Metrics in K-Means
345Feature Scaling for K-Means
346Implementing K-Means with Scikit-learn
347Limitations of K-Means and Motivation for Density-Based Methods
348DBSCAN: Core Concepts and Definitions
349DBSCAN Algorithm Step-by-Step
350Choosing Epsilon and MinPts Parameters
351DBSCAN Advantages and Handling Arbitrary Shapes
352DBSCAN Limitations and Failure Cases
353HDBSCAN: Hierarchical Density-Based Clustering
354Implementing and Evaluating Density-Based Clustering
355Hierarchical Clustering Overview
356Dendrograms and Tree Representations
357Linkage Criteria: Single, Complete, and Average
358Ward's Linkage and Variance Minimization
359Distance Metrics for Hierarchical Clustering
360Agglomerative Clustering Algorithm
361Computational Complexity and Scalability
362Implementing Hierarchical Clustering with Scikit-learn
363From K-Means to Probabilistic Clustering
364Gaussian Distribution as Cluster Model
365Mixture Model Definition
366Likelihood Function for GMMs
367The Expectation-Maximization Algorithm
368E-Step: Computing Responsibilities
369M-Step: Updating Parameters
370Model Selection: Choosing Number of Components
371Covariance Structure Constraints
372GMM Implementation and Applications
373What is Anomaly Detection?
374Statistical Approaches to Anomaly Detection
375Density-Based Anomaly Detection
376Isolation Forest Algorithm
377One-Class SVM for Novelty Detection
378Autoencoders for Anomaly Detection
379Evaluation Metrics for Anomaly Detection
380Anomaly Detection in Practice

← Machine Learning and Deep Learning

Lesson 356 of 3,538·8. Unsupervised Learning: ClusteringPro lesson

Dendrograms and Tree Representations

Interpreting dendrograms to visualize cluster merges and determine optimal number of clusters by cutting the tree.

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