CSE-435 Data Mining
E-mail: atik@cse.green.edu.bd
🕾 Mob. +8801912961096
Room: A-510 Desk No. : 08
Class Routine – Fall 2025 Semester
| Day | 08:30-10:00 | 10:00-11:30 | 11:30-1:00 | Break | 01:30-03:00 | 03:00-04:30 | 06:00-07:30 | 07:30-09:00 |
|---|---|---|---|---|---|---|---|---|
| Sat | CSE-435 221_D6 J-105 | CSE-435 221_D2 K-104 | CSE-436 221_D6 A-501 | Â | CSE-436 221_D16 A-503 | Â | Â | Â |
| Sun | Â | Â | TUTOR TIME | Â | TUTOR TIME | Â | Â | Thesis/Project Meeting Online |
| Mon | Â | Â | TUTOR TIME | Â | CSE-104 250_D6 A-502 | CSE-104 250_D6 A-502 | CSE-435 221_D6 Online | CSE-435 221_D2 Online |
| Tue | CSE-436 222_D1 J-103 | Â | TUTOR TIME | Â | TUTOR TIME | Â | Â | PSD-400 221_D2 Online |
| Wed | Â | Â | Â | Â | Â | Â | Â | Â |
Note: Thursday and Friday Weekend
Topic Outline
| Lecture | Selected Topic | Article / Chapter | Problems | Slides | Math Note |
|---|---|---|---|---|---|
| (1–3) | Introduction, What is Data Mining? Essential steps in Knowledge Discovery; Diversity of data types; Mining various kinds of knowledge; Data mining applications and impact on society | Ch. 1 | Ex. 1.9 | Slides | – |
| (4–7) | Data Types, Statistics of Data, Similarity and Distance Measures, Data Quality; Graphic displays of basic statistics of data; Data Cleaning and Integration | Ch. 2 | Ex. 2.8 | Slides | Math Note |
| (8–9) | Data Transformation, Dimensionality Reduction (PCA), Feature Selection | Ch. 2 | Ex. 2.8 | Slides | – |
| (10) | Pattern Mining: Basic Concepts and Methods; Frequent Itemset Mining Methods | Ch. 4 | Ex. 4.5 | Slides | Math Note |
| (11–12) | Mining Frequent Patterns with Apriori Algorithm and Pattern Growth Approach | Ch. 4 | Ex. 4.5 | Slides | Math Note |
| (13–14) | Mining Frequent Itemsets using the Vertical Data Format; Mining Closed and Max Patterns | Ch. 4 | Ex. 4.5 | Slides | Math Note |
| (15) | Which Patterns Are Interesting? — Pattern Evaluation Methods | Ch. 4 | Ex. 4.5 | Slides | Math Note |
|  | Midterm Examination | – | – | – | – |
| (16–19) | Classification: Basic Concepts and Methods; Decision Tree Induction, Attribute Selection Measures, Tree Pruning | Ch. 6 | Ex. 6.9 | Slides | – |
| (20) | Model Evaluation and Selection (Metrics, Holdout Method, Random Subsampling, Cross-Validation, Bootstrap etc.) | Ch. 6 | Ex. 6.9 | Slides | – |
| (21–22) | Ensemble Methods (Bagging, Boosting, Random Forests); Improving Classification Accuracy for Imbalanced Data | Ch. 6 | Ex. 6.9 | Slides | – |
| (23) | Support Vector Machines (Linear & Nonlinear Models), Kernel Functions | Ch. 7 | Ex. 7.9 | – | – |
| (24) | Classification with Weak Supervision (Active Learning, Transfer Learning, Distant Supervision, Zero-Shot Learning) | Ch. 7 | Ex. 7.9 | – | – |
| (25–26) | Cluster Analysis: Basic Concepts and Methods; Partitioning Methods (k-Medoids, k-Modes) | Ch. 8 | Ex. 8.7 | – | – |
| (27–29) | Hierarchical Clustering, Density-Based Clustering (DBSCAN), Evaluation of Clustering Results | Ch. 8 | Ex. 8.7 | – | – |
| (30) | Presentation and Assignment Submission | – | – | – | – |