Offered: Fall 2025 (current)
Fundamentals of Data Mining: data types, patterns, challenges, knowledge discovery process, data quality assessment; Data Preprocessing: cleaning, transformation, normalization, feature selection, handling missing values; Association Rule Mining: Apriori algorithm, FP-growth, pattern evaluation, support and confidence metrics; Classification Methods: decision trees, Naïve Bayes, support vector machines, ensemble methods, evaluation metrics; Clustering Techniques: K-means, hierarchical clustering, DBSCAN, partitioning algorithms, cluster validation; Anomaly Detection: outlier analysis, statistical methods, distance-based techniques, density-based approaches; Dimensionality Reduction: PCA, t-SNE, feature extraction, manifold learning techniques; Time-Series Mining: forecasting, trend analysis, sequence patterns, temporal association rules; Text Mining: document representation, sentiment analysis, topic modeling, NLP techniques; Graph and Web Mining: social networks, link prediction, community detection, web usage patterns; Big Data Analytics: scalable mining algorithms, distributed computing frameworks.
The core objectives of this course are to:
To introduce core concepts, methodologies, and challenges in data mining
To develop proficiency in data cleaning, transformation, and feature selection
To learn and implement algorithms for discovering frequent patterns and associations in data
To gain expertise in various classification techniques and their evaluation metrics
To understand and apply different clustering algorithms for grouping similar data points
To identify outliers and anomalies using statistical and distance-based methods
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