BOE095

Data Warehousing & Data Mining

L-T-P3-0-0 Credits3 Session2025-26 TypeTheory
Appears in 36 semesters · 36 branches

B.Tech Civil Engineering · Sem 8 (Open Elective-III) · B.Tech Mechanical Engineering · Sem 8 (Open Elective-III) · B.Tech Computer Science & Engineering · Sem 8 (Open Elective-III) · B.Tech Information Technology · Sem 8 (Open Elective-III) · B.Tech Artificial Intelligence & Machine Learning · Sem 8 (Open Elective-III) · B.Tech Artificial Intelligence & Data Science · Sem 8 (Open Elective-III) · B.Tech CSE (Artificial Intelligence) · Sem 8 (Open Elective-III) · B.Tech CSE (AI & ML) · Sem 8 (Open Elective-III) · B.Tech CSE (Data Science) · Sem 8 (Open Elective-III) · B.Tech CSE (Cyber Security) · Sem 8 (Open Elective-III) · B.Tech CSE (Internet of Things) · Sem 8 (Open Elective-III) · B.Tech Computer Science & Design · Sem 8 (Open Elective-III) · B.Tech Computer Science (Hindi) · Sem 8 (Open Elective-III) · B.Tech Computer Science & Business Systems · Sem 8 (Open Elective-III) · B.Tech Electrical Engineering · Sem 8 (Open Elective-III) · B.Tech Electrical & Electronics Engineering · Sem 8 (Open Elective-III) · B.Tech Electrical & Computer Engineering · Sem 8 (Open Elective-III) · B.Tech Electronics & Communication Engineering · Sem 8 (Open Elective-III) · B.Tech Electronics & Instrumentation / Instrumentation & Control · Sem 8 (Open Elective-III) · B.Tech Electronics & Computer Engineering · Sem 8 (Open Elective-III) · B.Tech ECE (Advanced Communication Technology) · Sem 8 (Open Elective-III) · B.Tech Electronics Engineering (VLSI Design & Technology) · Sem 8 (Open Elective-III) · B.Tech Automobile Engineering · Sem 8 (Open Elective-III) · B.Tech Aeronautical Engineering · Sem 8 (Open Elective-III) · B.Tech Mechanical & Industrial Engineering · Sem 8 (Open Elective-III) · B.Tech Production & Industrial Production Engineering · Sem 8 (Open Elective-III) · B.Tech Manufacturing Technology · Sem 8 (Open Elective-III) · B.Tech Mining Engineering · Sem 8 (Open Elective-III) · B.Tech Chemical Engineering · Sem 8 (Open Elective-III) · B.Tech Plastic Engineering · Sem 8 (Open Elective-III) · B.Tech Food Technology · Sem 8 (Open Elective-III) · B.Tech Textile Technology · Sem 8 (Open Elective-III) · B.Tech Handloom & Textile Technology · Sem 8 (Open Elective-III) · B.Tech Carpet & Textile Technology · Sem 8 (Open Elective-III) · B.Tech Biotechnology · Sem 8 (Open Elective-III) · B.Tech Agricultural Engineering · Sem 8 (Open Elective-III)

Syllabus

5 units · 40 lecture hours
Unit1

Unit 1: Data Warehousing

8 hrs

Overview, Definition, Data Warehousing Components, Building a Data Warehouse, Warehouse Database, Mapping the Data Warehouse to a Multiprocessor Architecture, Difference between Database System and Data Warehouse, Multi Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept.

Unit2

Unit 2: Data Warehouse Process and Technology

8 hrs

Warehousing Strategy, Warehouse /management and Support Processes, Warehouse Planning and Implementation, Hardware and Operating Systems for Data Warehousing, Client/Server Computing Model & Data Warehousing. Parallel Processors & Cluster Systems, Distributed DBMS implementations, Warehousing Software, Warehouse Schema Design

Unit3

Unit 3

8 hrs

Data Mining: Overview, Motivation, Definition & Functionalities, Data Processing, Form of Data Pre-processing, Data Cleaning: Missing Values, Noisy Data, (Binning, Clustering, Regression, Computer and Human inspection), Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Discretization and Concept hierarchy generation, Decision Tree

Unit4

Unit 4: Classification

8 hrs

Definition, Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining Class comparisons, Statistical measures in large Databases, Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree-Based Algorithms. Clustering: Introduction, Similarity and Distance Measures, Hierarchical and Partitional Algorithms. Hierarchical Clustering- CURE and Chameleon. Density Based Methods DBSCAN, OPTICS. Grid Based Methods- STING, CLIQUE. Model Based Method – Statistical Approach, Association rules: Introduction, Large Item sets, Basic Algorithms, Parallel and Distributed Algorithms, Neural Network approach.

Unit5

Unit 5: Data Visualization and Overall Perspective

8 hrs

Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAP Servers, ROLAP, MOLAP, HOLAP, Data Mining interface, Security, Backup and Recovery, Tuning Data Warehouse, Testing Data Warehouse. Warehousing applications and Recent Trends: Types of Warehousing Applications, Web Mining, Spatial Mining and Temporal Mining.

Suggested Readings

  1. Alex Berson, Stephen J. Smith “Data Warehousing, Data-Mining & OLAP”, McGrawHil.
  2. Mark Humphries, Michael W. Hawkins, Michelle C. Dy, “Data Warehousing: Architecture and Implementation”, Pearson Education..
  3. I. Singh, “Data Mining and Warehousing”, Khanna Publishing House.
  4. Margaret H. Dunham, S. Sridhar,”Data Mining:Introductory and Advanced Topics” Pearson Education.

Official AKTU syllabus

This subject is on page 30, 31 of the original AKTU PDF.

Official AKTU syllabus PDF ↗