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Elective Data Warehousing and Data Mining

Data Warehousing and Data Mining notes

9 chapter notes, in syllabus order. Each starts with the key points.

Unit 1 · 5 hrs

Data Warehousing: Concepts, Architecture & OLAPUnit 1 of Data Warehousing and Data Mining introduces the core principles of data warehousing, including its definition, architecture, types, and comparison with operational databases, along with OLAP fundamentals and real-world applications in decision support systems.14 min read

Unit 2 · 2 hrs

Data Mining: Definitions, Tasks, Techniques & Real-World ImpactUnit 2 of Data Warehousing and Data Mining introduces the core concepts of data mining—its definition, key tasks (classification, clustering, association, prediction), techniques (supervised vs. unsupervised), and real-world applications in Nepalese and global industries. This note explains how data mining extracts hid14 min read

Unit 3 · 3 hrs

Data Preprocessing: Cleaning, Integration, Transformation & ReductionUnit 3 of Data Warehousing and Data Mining covers the critical preprocessing steps—data cleaning, integration, transformation, and reduction—that prepare raw data for mining and warehousing, with real-world examples from Nepalese apps like eSewa and Daraz.13 min read

Unit 4 · 4 hrs

Data Cube: OLAP, Aggregation, Drill-Down & SlicingUnit 4 of Data Warehousing and Data Mining explains data cubes, their structure, operations (roll-up, drill-down, slice/dice), and how they enable OLAP for fast analytics. Covers multidimensional modeling, aggregation hierarchies, and real-world applications in business intelligence.6 min read

Unit 5 · 6 hrs

Frequent Pattern Mining: Algorithms, Trees & ApplicationsUnit 5 of Data Warehousing and Data Mining covers frequent pattern mining, including Apriori, FP-Growth, and association rules, with real-world examples from e-commerce (Daraz, Amazon) and transactional systems (Khalti, banks). Learn how to extract meaningful patterns from large datasets, calculate support/confidence, 19 min read

Unit 6 · 10 hrs

Classification & Prediction: Models, Trees, Rules & EvaluationUnit 6 of Data Warehousing and Data Mining covers supervised learning techniques—decision trees, rule-based classifiers, Bayesian networks, neural networks, and evaluation metrics (accuracy, precision, recall)—with real-world applications in fraud detection, customer segmentation, and predictive maintenance.7 min read

Unit 7 · 8 hrs

Cluster Analysis: Algorithms, Applications & EvaluationUnit 7 of Data Warehousing and Data Mining explores clustering techniques (partitioning, hierarchical, density-based), distance metrics, evaluation metrics (silhouette score, Davies-Bouldin index), and real-world applications in customer segmentation, anomaly detection, and image compression.13 min read

Unit 8 · 5 hrs

Graph Mining & Social Networks: Models, Algorithms & ApplicationsUnit 8 of Data Warehousing and Data Mining explores graph-based data structures, mining algorithms for networks, and real-world applications in social networks, recommendation systems, and fraud detection—covering graph representations, traversal techniques, community detection, and path analysis with worked examples t17 min read

Unit 9 · 2 hrs

Spatial, Multimedia, Text & Web Data MiningUnit 9 of Data Warehousing and Data Mining explores techniques for extracting insights from unstructured data—geospatial patterns (GPS, maps), multimedia (images, audio), text (documents, social media), and web data (logs, hyperlinks)—using feature extraction, clustering, classification, and specialized algorithms like11 min read