MCA C3001
Data Warehousing & Data Mining
Notes are on their way.
For now, use the syllabus outline to see what this subject covers.
Syllabus topics
Fundamentals of data mining, Data Mining Functionalities, Classification of Data Mining systems, Maj or issues in Data Mining, Data Warehouse and OLAP Technology for Data Mining Data Warehouse, Multidimensional Data Model, Data Warehouse Architecture, Data Warehouse Implementation, Further Development of Data Cube Technology, From Data Warehousing to Data Mining . [Q-1]
Notes will be added here.
Syllabus: supplied PDF, page 20.
Syllabus topics
Needs Preprocessing the Data, Data Cleaning, Data Integration and Transformation, Data Reduction, Discretization and Concept Hierarchy Generation, Online Data Storage.Data Mining Primitives, Languages, and System Architectures: Data Mining Primitives, Data Mining Query Languages, Designing Graphical User Interfaces Based on a Data Mining Query Language Architectures of Data Mining Systems. [Q-2]
Notes will be added here.
Syllabus: supplied PDF, page 20.
Syllabus topics
Characterization and Comparison: Data Generalization and Summarization -Based Characterization, Analytical Characterization: Analysis of Attribute Relevance, Mining Class Comparisons: Discriminating between Different Classes, Mining Descriptive Statistical Measures in Large Databases. [Q-1]
Notes will be added here.
Syllabus: supplied PDF, page 20.
Syllabus topics
Association Rule Mining, Mining Single -Dimensional Boolean Association Rules from Transactional Databases. Mining Multilevel Association Rules from Transaction Databases, Mining Multidimensional Association Rules from Relational Databases and Data Warehouses, From Association Mining to Correlation Analysis, Constraint - Based Association Mining. [Q-2]
Notes will be added here.
Syllabus: supplied PDF, page 20.
Syllabus topics
Issues Regarding Classification and Prediction, Classification by Decision Tree Induction, Bayesian Classification, Classification by Back propagation, Classification Based on Concepts from Associatio n Rule Mining, Other Classification Methods, Prediction, Classifier Accuracy. Cluster Analysis Introduction: Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods, Density -Based Methods, Grid -Based Methods, Model -Based Clustering Methods, Outlier Analysis. [Q-2]
Notes will be added here.
Syllabus: supplied PDF, page 20.
Check your university’s syllabus for the topics covered in your exams.