Workshop Content

Day 1: Introduction to Cluster Analysis and Core Techniques

• Overview of Cluster Analysis: Discuss the role of clustering in data analysis, including types of clustering problems and applications across various industries.
• Core Clustering Techniques: Cover foundational methods such as hierarchical clustering (agglomerative and divisive), k-means, and density-based clustering (e.g., DBSCAN). Include practical tips on choosing the appropriate method based on data type and analysis goals.
• Evaluation Metrics: Introduce key metrics for assessing clustering quality, like silhouette scores, within-cluster sum of squares, and external validation indices (e.g., purity, Rand index).
• Case Studies and Hands-On Practice: Present a case study demonstrating basic clustering techniques, then guide participants in applying these methods to real datasets using MATLAB and R.

Day 2: Fuzzy Clustering and K-Prototype Methods

• Fuzzy Clustering: Explain fuzzy clustering, focusing on fuzzy c-means, which allows for overlapping cluster memberships and is useful when data points do not strictly belong to a single cluster.
• K-Prototype Clustering for Mixed Data: Dive into k-prototype clustering, a method designed for mixed data types (numerical and categorical). Discuss how this approach balances different data types in a way that traditional algorithms like k-means cannot.
• Case Studies and Hands-On Practice: Participants work with datasets containing mixed data types and fuzzy clusters. Case studies may include customer profiling where customer data contain both categorical (e.g., preferences) and numerical features (e.g., income).

Day 3: Advanced Topics and Applications to Large Datasets

• Regularization Techniques: Introduce regularization in clustering, such as L0 penalty methods that help manage sparsity and noise in high-dimensional data.
• Time Series Clustering: Cover time series clustering techniques like Dynamic Time Warping (DTW) and how to group sequences with varying lengths or patterns. Discuss applications in finance, healthcare, and IoT data.
• Large-Scale Data Clustering: Discuss clustering challenges with large datasets and introduce techniques such as mini-batch k-means, scalable hierarchical clustering, and clustering in distributed computing environments.
• Case Studies and Hands-On Practice: Participants work on case studies related to time series data (e.g., stock prices or sensor data) and high-dimensional datasets, practicing large-scale clustering approaches.

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