Overview
Clustering is a fundamental technique in unsupervised learning that allows data scientists to group similar data points without prior labels. It plays a crucial role in exploratory data analysis, helping to uncover hidden patterns and insights within datasets. By using various algorithms like K-mean...
Key Terms
Example: Customer clustering based on purchasing behavior.
Example: K-means can be used to segment customers into different groups.
Example: In K-means, the centroid is recalculated after each iteration.
Example: A higher Silhouette Score indicates better-defined clusters.
Example: DBSCAN can find clusters of varying shapes and sizes.
Example: Euclidean distance is commonly used in clustering.
Related Topics
Dimensionality Reduction
Techniques to reduce the number of features in data while preserving important information.
intermediateSupervised Learning
A type of machine learning where the model is trained on labeled data.
intermediateAnomaly Detection
Identifying unusual patterns that do not conform to expected behavior.
intermediateKey Concepts
Data PointsCentroidsDistance MetricsCluster Validation