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AKTU Data Warehousing and Data Mining (BCS058) Syllabus PDF – B.Tech 3rd Year
Data Warehousing and Data Mining BCS058
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Official AKTU syllabus of Data Warehousing and Data Mining (BCS058) for B.Tech 3rd Year – 3 credits, L-T-P 3-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
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Syllabus — Data Warehousing and Data Mining
Download PDFBCS058 · 3 credits · L-T-P 3-0-0
Course Outcomes
At the end of course , the student will be able to understand
| CO 1 | Be familiar with mathematical foundations of data mining tools.. | K1 , K2 |
| CO 2 | Understand and implement classical models and algorithms in data warehouses and data mining | K3 |
| CO 3 | Characterize the kinds of patterns that can be discovered by association rule mining, classification and clustering. | K1 , K2 |
| CO 4 | Master data mining techniques in various applications like social, scientific and environmental context. | K3 |
| CO 5 | Develop skill in selecting the appropriate data mining algorithm for solving practical problems. | K1 , K2 |
Course Content
L-T-P: 3-0-0
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | Data Warehousing: 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 | 08 |
| II | Data Warehouse Process and Technology: 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, | 08 |
| III | 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. | 08 |
| IV | Classification: 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. | 08 |
| V | Data Visualization and Overall Perspective: 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 | 08 |
Text Books
- Alex Berson, Stephen J. Smith “Data Warehousing, Data-Mining & OLAP”, TMH
- Mark Humphries, Michael W. Hawkins, Michelle C. Dy, “ Data Warehousing: Architecture and Implementation”, Pearson
- Margaret H. Dunham, S. Sridhar,”Data Mining:Introductory and Advanced Topics” Pearson Education
- Arun K. Pujari, “Data Mining Techniques” Universities Press
- Pieter Adriaans, Dolf Zantinge, “Data-Mining”, Pearson Education
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