Executive Development Programme in Data Clustering Techniques
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⢠Introduction to Data Clustering Techniques: Defining clustering, understanding the importance and applications of data clustering, differentiating clustering from classification, and introducing various clustering approaches.
⢠Distance Measures: Learning about different distance measures, such as Euclidean, Manhattan, and Chebyshev distances, and their impact on clustering results.
⢠Partitioning Methods: Exploring clustering methods like K-means, K-medoids, and CLARA, focusing on their assumptions, advantages, and limitations.
⢠Hierarchical Clustering: Delving into hierarchical clustering techniques like single-linkage, complete-linkage, and group-average methods, and understanding their pros and cons.
⢠Density-Based Clustering: Examining DBSCAN, OPTICS, and Mean-Shift algorithms, emphasizing their capacity to discover clusters of arbitrary shapes and handle noise.
⢠Model-Based Clustering: Introducing statistical approaches for clustering, including Gaussian mixture models, and understanding their assumptions and use cases.
⢠Evaluation and Validation: Learning about internal and external validation methods, such as silhouette scores, elbow method, and adjusted Rand index, to assess clustering performance.
⢠Scalability and Parallelism in Data Clustering: Discussing techniques to handle large datasets, such as sampling, dimensionality reduction, and parallel processing in clustering algorithms.
⢠Special Topics in Data Clustering: Exploring advanced clustering techniques, like subspace clustering, spectral clustering, and ensemble clustering, and their applicability in various domains.
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