DATA WAREHOUSING and DATA MINING AKTU Notes PDF Download KOE093
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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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.