Lecture 11 explored the ethical, legal, and privacy implications of data mining and modern AI systems. Covering laws (GDPR, CCPA, HIPAA), privacy risks, algorithmic bias, fairness, transparency, security threats, and privacy-preserving solutions, this lecture prepares students to think critically about…
Lecture 10 provides a complete hands-on guide to Data Mining implementation using Python with Pandas, NumPy, Matplotlib, Scikit-learn, and MLxtend. Includes preprocessing, classification, clustering, association rule mining, examples, diagrams, code, and evaluation techniques. Python has become the most widely used…
Lecture 9 explores current Data Mining trends and future research frontiers, including big data, AI integration, IoT, cloud analytics, privacy-preserving models, multimodal data, quantum computing, and modern industry applications. Data Mining is no longer limited to simple classification, clustering, or…
Lecture 8 explains Web Mining and Social Network Mining, including content mining, structure mining, usage mining, link analysis, PageRank, HITS, graph theory, community detection, sentiment analysis, examples, diagrams, and Python code. The modern web contains billions of interconnected documents, pages,…
Lecture 7 explains Outlier and Anomaly Detection in Data Mining including statistical methods (Z-score, IQR), density-based LOF, clustering techniques, and machine learning approaches like Isolation Forest. Includes diagrams, examples, Python code, and case studies. Outlier and Anomaly Detection plays a…
Lecture 6 explains clustering in Data Mining, including K-Means, K-Medoids, hierarchical clustering, and DBSCAN with diagrams, algorithms, examples, Python code, real-world applications, and comparisons for BS CS, BS AI, BS IT & Data Science students. Clustering is one of the…