If you are a B.Tech 4th-year student at Dr. A.P.J. Abdul Kalam Technical University (AKTU) and you are looking for comprehensive and well-structured notes for the subject KOE093: Data Warehousing & Data Mining, you can now download them in PDF format. These notes cover the entire syllabus in a concise and easy-to-understand manner.
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Syllabus Covered in the Notes
Unit I: Data Warehousing
- Overview and 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, snowflakes, fact constellations, concept
Unit II: Data Mining
- Overview and motivation
- Definition and functionalities
- Data processing and 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
Unit III: 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 and data warehousing
- Parallel processors and cluster systems
- Distributed DBMS implementations
- Warehousing software
- Warehouse schema design
Unit IV: Classification and Clustering
- 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
Unit V: Data Visualization and Overall Perspective
- Aggregation and historical information
- Query facility and 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
Make sure to review all the sections thoroughly to prepare for your exams and to gain a comprehensive understanding of data warehousing and data mining as per the AKTU syllabus.