Data Warehousing and Data Mining (B.Tech)

Departmental Elective-I: data warehouse design, OLAP, association rules, classification and clustering.

6 MonthsDuration 5Chapters27Topics

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Syllabus · पाठ्यक्रम

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01Unit 1: Data Warehousing and Multidimensional Model 6 topics

Definition and components of a data warehouse
Building a data warehouse and mapping to multiprocessor architecture
Database system vs data warehouse
Multidimensional data model and data cubes
Star, snowflake and fact constellation schemas
Concept hierarchy, three-tier architecture and data marts

02Unit 2: Data Warehouse Process, Technology and OLAP 4 topics

Warehousing strategy and data management
OLAP servers: ROLAP, MOLAP, HOLAP
OLAP operations: roll-up, drill-down, slice, dice, pivot
Data warehouse backup, testing and tuning

03Unit 3: Data Mining and Pre-processing 5 topics

Data mining definition and functionalities
Data cleaning: missing values and noisy data (binning, regression, clustering)
Data integration and transformation (normalisation)
Data reduction: cube aggregation, dimensionality reduction, compression, numerosity reduction
Discretisation and concept hierarchy generation

04Unit 4: Classification and Association Rules 6 topics

Data generalisation and attribute relevance analysis
Statistical, distance-based and decision tree classification
Mining class comparisons
Association rules: support and confidence
Apriori algorithm
FP-growth

05Unit 5: Clustering and Advanced Mining 6 topics

Similarity and distance measures
Partitioning: K-means and K-medoids
Hierarchical clustering: agglomerative, divisive, CURE, Chameleon
Density based: DBSCAN, OPTICS; grid based: STING, CLIQUE
Model based clustering
Time series, web and spatial mining

About

You will be able to

Differentiate a data warehouse from an operational database and explain warehouse architecture
Design star, snowflake and fact constellation schemas and data cubes
Apply OLAP operations: roll-up, drill-down, slice, dice and pivot; compare ROLAP, MOLAP and HOLAP
Pre-process data: cleaning, integration, transformation, reduction and discretisation
Apply decision tree classification and Apriori / FP-growth association mining
Apply partitioning, hierarchical, density and grid based clustering; explain web, spatial and time series mining

Who it is for

B.Tech Information Technology students of Semester 5 (AKTU)
Students of other universities studying the same subject who want unit-wise coaching

What you need

Database Management System
Basic statistics

Questions

Which syllabus does the Data Warehousing and Data Mining course follow?

It follows the AKTU (Dr. A.P.J. Abdul Kalam Technical University, Lucknow) B.Tech syllabus for Data Warehousing and Data Mining. If your own university's syllabus differs, the centre follows your university syllabus.

Will this help in the university examination?

Yes. Teaching is unit-wise as per the AKTU scheme, with previous-year question practice, MCQ tests and doubt sessions before the semester exam.

Are tools such as Weka used?

Yes. Along with theory and numericals, Weka / Python demonstrations of classification, association and clustering are shown.