Certificate in Clustering Fundamentals: Results-Oriented Approach
-- ViewingNowThe Certificate in Clustering Fundamentals: Results-Oriented Approach is a comprehensive course designed to equip learners with essential skills in data analysis and machine learning. This certification focuses on clustering, a critical technique for grouping and segmenting data to derive meaningful insights.
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โข Introduction to Clustering: Defining clustering, types of clustering, real-world applications, and benefits.
โข Data Preprocessing: Data cleaning, normalization, transformation, and dimensionality reduction.
โข Measures of Similarity and Dissimilarity: Distance metrics, linkage criteria, and similarity measures.
โข Partitioning Methods: K-means, K-medoids, and hierarchical clustering.
โข Density-Based Methods: DBSCAN, OPTICS, and HDBSCAN.
โข Hierarchical Agglomerative Clustering (HAC): Agglomerative clustering, divisive clustering, and linkage methods (single, complete, and average).
โข Clustering Validation: Internal and external validation, cluster stability, and interpretability.
โข Fuzzy Clustering: Fuzzy C-means, fuzzy clustering evaluation, and real-world applications.
โข Streaming and Incremental Clustering: CluStream, DenStream, and BIRCH.
โข Scalable Clustering Algorithms: Distributed computing, parallel processing, and GPU-based methods.
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