Syllabus Data Mining Techniques - (BE05000181) Total Credits = TH / 30 Assessment Pattern and Marks Total Marks Theory Tutorial / Practical ESE (E) PA (M) PA (I) PBL (I) ESE (V) 03 70 30 20 30 50 200 Unit No. Content 1. Data Mining, Knowledge Discovery in Databases (KDD) Process, Data Mining Functionalities, Applications of Data Mining, Data Mining Task Primitives, Major Issues in Data Mining, Introduction to Statistical Measures : measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), and basic data distribution concepts. (Chapter - 1) 2. Data Pre-processing : Data Cleaning, Data Integration, Data Reduction, Data Transformation and Data Discretization. Introduction of data mining tool such as orange. (Chapter - 2) 3. Mining Frequent Patterns, Associations, and Correlations : Basic Concepts and Methods : Market Basket Analysis, Frequent Itemsets, Closed Itemsets, and Association Rule, Frequent Itemset Mining Methods, mining various kind of association rules, from association mining to correlation analysis, Which Patterns Are Interesting? - Pattern Evaluation Methods.(Chapter - 3) 4. Classification and Prediction : Classification vs. prediction, Issues regarding classification and prediction, Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree Induction, Bayes Classification Methods, Rule-Based Classification, Model Evaluation and Selection, Techniques to Improve Classification Accuracy, Bayesian Belief Networks, Classification by Backpropagation, Support Vector Machines, Linear and nonlinear regression, Logistic Regression, Introduction of data mining tool such as WEKA. (Chapter - 4) 5. Cluster Analysis : What Is Cluster Analysis? Requirements for Cluster Analysis, Partitioning Methods : k-Means : A Centroid-Based Technique, k-Medoids : A Representative Object-Based Technique, Hierarchical Methods : Agglomerative and Divisive Hierarchical Clustering, Density-Based Methods : DBSCAN. (Chapter - 5) 6. Introduction to hyperparameters : Need for and importance of hyperparameter tuning, Overfitting and underfitting, Hyperparameters in algorithms, Different methods of Hyperparameter tuning techniques : Explainable AI in Data Mining : Introduction to Explainable AI (XAI), Need for explainability in data mining, Black-box vs interpretable models, Explainable AI techniques. (Chapter - 6) 7. Advance data mining techniques : Web Mining, Spatial Mining, Temporal Mining, Spatial temporal Mining, Stream Mining, Text Mining, And Multimedia Mining. Applications of Distributed and parallel Data Mining. (Chapter - 7)
