Data Mining and Warehouse Syllabus , Mid Exam Papers and Public Exam April_2018 Paper

Also Read

RAYALASEEMA UNIVERSITY  

UG VI SEMESTER PAPER

Data Mining and Ware Housing

Syllabus:

Unit-I:

Introduction to Data mining: fundamentals of data mining, data mining functionalities, data and attributes types, statistical description of data, Data pre-processing, data cleaning, data integration, data reduction data transformation  and data discretization.

Unit-II:                 

Data Warehousing: basic concepts, data warehouse modelling data cube and OLAP, data warehouse design and implementation.

Unit-III:

Mining Frequent Patterns and Associations: basic methods, frequent item set mining methods any two algorithms, pattern evaluation methods.

Unit-IV:

Classification: basic concepts, decision tree induction, Bayes classification, any two advanced methods, model evaluation.

Unit-V:

Cluster Analysis: basic concepts, clustering structures, major clustering approaches, partitioning methods, hierarchical methods, density based methods, the expectation maximization method, cluster based outlier detection essential reading.

Public Exam Paper April_2018 

Mid Exam Papers

RAYALASEEMA UNIVERSITY  

UG VI SEMESTER PAPER

Data Mining and Ware Housing

Syllabus:

Unit-I:

Introduction to Data mining: fundamentals of data mining, data mining functionalities, data and attributes types, statistical description of data, Data pre-processing, data cleaning, data integration, data reduction data transformation  and data discretization.

Unit-II:                 

Data Warehousing: basic concepts, data warehouse modelling data cube and OLAP, data warehouse design and implementation.

Unit-III:

Mining Frequent Patterns and Associations: basic methods, frequent item set mining methods any two algorithms, pattern evaluation methods.

Unit-IV:

Classification: basic concepts, decision tree induction, Bayes classification, any two advanced methods, model evaluation.

Unit-V:

Cluster Analysis: basic concepts, clustering structures, major clustering approaches, partitioning methods, hierarchical methods, density based methods, the expectation maximization method, cluster based outlier detection essential reading.

Public Exam Paper April_2018 

Mid Exam Papers