BiteSizedChunks.comLearn one small thing at a time.

Course contentsShow

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

Distance Metrics in K-Means

Why Euclidean distance is standard, alternatives like Manhattan distance, and the impact of metric choice.

This lesson is for subscribers

You've completed the free preview. Subscribe to unlock every lesson in every course.

See pricing Back to course