CSE-435 Data Mining
E-mail: atik@cse.green.edu.bd
🕾 Mob. +8801912961096
Room: A-510 Desk No. : 08
Class Routine – Summer 2026 Semester
| Day | 08:30-10:00 | 10:00-11:30 | 11:30-1:00 | Break | 1:30-03:00 | 3:00-4:30 |
|---|---|---|---|---|---|---|
| Sat | CSE 435 223_D1 K-105 | CSE 435 223_D1 K-105 | Â | Â | Tutor Time | Research Meeting |
| Sun | Â | CSE 403 232_D2 K-105 | Tutor Time | Â | CSE 315 241_D2 K-108 | Research Meeting |
| Mon | CSE 404 232_D4 L-106 | CSE 404 232_D4 L-106 | Research Meeting | Â | CSE 310 Old Batch A-502 | CSE 310 Old Batch A-502 |
| Tue | CSE 315 241_D2 K-108 | CSE 403 232_D2 K-105 | Research Meeting | Â | Tutor Time | Tutor Time |
| Wed | Â | Â | Â | Â | Â | Â |
Topic Outline
| Lecture | Selected Topic | Article / Chapter | Problems | Slides | Math Note |
|---|---|---|---|---|---|
| (1–2) | Introduction to Data Mining; What is Data Mining? Knowledge Discovery Process; Diversity of Data Types; Mining Different Kinds of Knowledge; Applications of Data Mining and Societal Impact | Ch. 1 | Ex. 1.2–1.5, 1.7, 1.9 | Slides | – |
| (3–6) | Data Types; Statistical Description of Data; Similarity and Distance Measures; Data Quality; Data Visualization; Data Cleaning and Data Integration | Ch. 2, Ch. 3 | Ex. 2.2–2.4, 2.6, 2.8 | Slides | Math Note |
| (7–8) | Data Transformation; Feature Selection; Dimensionality Reduction using PCA; t-SNE for Data Visualization | Ch. 3, Ch. 9 | Ex. 3.1–3.8, 9.1–9.3 | Slides | – |
| (9) | Pattern Mining: Basic Concepts and Frequent Itemset Mining Methods | Ch. 6.1 | – | Slides | Math Note |
| (10–11) | Mining Frequent Patterns using Apriori Algorithm and Pattern Growth Approach (FP-Growth) | Ch. 6.2 | Ex. 6.1–6.5 | Slides | Math Note |
| (12–13) | Vertical Data Format Mining; Closed and Max Patterns; Pattern Evaluation and Interestingness Measures | Ch. 6.3 | – | Slides | Math Note |
|  | Midterm Examination | – | – | – | – |
| (14–16) | Cluster Analysis: Basic Concepts; Partitioning Methods (K-Means, K-Medoids, K-Modes) | Ch. 10.1–10.2 | Ex. 10.2–10.5 | – | – |
| (17–19) | Hierarchical Clustering; Density-Based Clustering (DBSCAN); Evaluation of Clustering Results | Ch. 10.3–10.4 | Ex. 10.6–10.7 | – | – |
| (20) | Outlier and Anomaly Detection: Data-Centric View | Supplementary | – | – | – |
| (21–22) | Text Mining and Text Preprocessing; Web Mining; Introduction to Big Data Analytics (Hadoop and Spark) | Supplementary | – | – | – |
| (23) | RNNs, Sequence-to-Sequence Learning, Attention Mechanism, Transformers and Their Applications | Supplementary | – | – | – |
| (24) | Recommendation Systems; Student Presentations; Assignment Submission; Course Wrap-Up | Supplementary | – | – | – |
|  | Final Examination | – | – | – | – |