R23
CSE · 3-1
✓ VERIFIED VS. PUBLISHED SYLLABUS 2026-07-09
Data Mining Lab JNTUK R23 · CSE · semester 3-1 · syllabus
This JNTUK R23 professional core course is listed for Computer Science and Engineering (CSE). It is listed in semester 3-1. It carries 1.5 credits. Its published Experiments section lists 8 activities or topic groups.
Verified vs. published syllabus
Checked
Offered in
| Branches | Semester | L | T | P | C |
|---|---|---|---|---|---|
| CSE | 3-1 | 0 | 0 | 3 | 1.5 |
SOURCE DOCKET
✓ Verified
Published evidence
- Source
- Official JNTUK syllabus jntuk.edu.in
- Checked
- Record
- 1 published section
Verification scopeSubject name, credits, unit-wise syllabus, and course outcomes are sourced directly from JNTUK's own official R23 course structure and syllabus document, not an autonomous college's adaptation.
What you'll be able to do
- Construct data warehouses, design multidimensional models (Star/Snowflake/Fact Constellation), and perform ETL and OLAP operations using industrial tools
- Explore and operate WEKA tools to load, preprocess, visualize datasets, and analyze attribute characteristics for machine learning tasks
- Apply data preprocessing, association rule mining (Apriori/FP-Growth), and interpretation of rules to derive meaningful insights from datasets
- Implement and evaluate classification and clustering algorithms (ID3, J48, NB, k-NN, k-means) using WEKA/Python/R, and compare model performance using ROC curves, entropy, SSE, and confusion matrices
- Develop machine learning programs in Python/Java/R for association mining, Naive Bayes, clustering, chi-square computation, and visualization using Matplotlib, and interpret results
Published practical or activity coverage: Experiments. Tick it off as you cover it — your progress stays on this device.