Data Mining Final Exam - Priority Map Overview
Post-Sessional (60% Weightage)
Topic | Weight | Importance | Focus Area | Likely Question Type
Naive Bayesian Classification | 60% | Critical | Concept + Formula | MCQs + Algorithm
Support Vector Machines (SVM) | 60% | Critical | Margin, Kernel Trick | MCQs + Theory
Flat Clustering | 60% | Critical | Algorithm + Elbow Method | MCQs + Algorithm
Hierarchical Clustering | 60% | Critical | Dendrograms, Linkage types | MCQs + Algorithm
Bagging & Boosting | 60% | Critical | Differences + Flowcharts | MCQs + Conceptual
Artificial Neural Networks (ANN) | 60% | Critical | Architecture + Forward Pass | MCQs + Basic Algorithm
Convolutional Neural Networks | 60% | Critical | Filters, Pooling | MCQs
Semi-Supervised Learning | 60% | Moderate | Difference from supervised | MCQs
Imbalanced Data | 60% | Moderate | SMOTE, Precision/Recall | MCQs
Sessional-II Topics (20% Weightage)
Topic | Importance | Focus Area | Likely Question Type
Decision Trees | High | ID3, Entropy, Gini Index | MCQs + Algorithm
Mining Sequence Patterns | Moderate | AprioriAll, SPADE | MCQs
Time Series Data | Low-Medium | Trends, Forecasting models | MCQs
Sessional-I Topics (20% Weightage)
Topic | Importance | Focus Area | Likely Question Type
Data Mining Introduction | Low | Definitions, KDD steps | MCQs only
Data Preprocessing | Moderate | Normalization, Cleaning | MCQs
Association Rule Mining | High | Support, Confidence, Lift | MCQs + Algorithm
Multiple Minimum Support | Moderate | Handling rare items | MCQs
Class Association Rules | Moderate | Extension of ARM | MCQs