JNTUH B.Tech 4th Year 1 sem Computer Science and Engineering R13 (4-1) Data Warehousing and Data Mining R13 syllabus.

JNTUH B.Tech 4th year (4-1) Data Warehousing and Data Mining gives you detail information of Data Warehousing and Data Mining R13 syllabus It will be help full to understand you complete curriculum of the year.

Objectives

Study data warehouse principles and its working learn data mining concepts understand association rules mining. Discuss classification algorithms learn how data is grouped using clustering techniques.

UNIT – I

Data warehouse : Introduction to Data warehouse, Difference between operational database systems and data warehouses. Data warehouse Characteristics, Data warehouse Architecture and its Components, Extraction – Transformation – Loading, Logical (Multi – Dimensional), Data Modelling, Schema Design, Star and Snow – Flake Schema, Fact Consultation, Fact Table, Fully Addictive, Semi – Addictive, Non Addictive Measures; Fact Consultation, Fact Table, Fully Addictive, Semi – Addictive, Non Addictive Measures; Fact – Less – Facts, Dimension Table Characteristics; OLAP Cube,OLAP Operations, OLAP Server Architecture – ROLAP, MOLAP and HOLAP.

Introducing to Data Mining : Introduction to Data Mining : Introducing, What is Data Mining Difference between operational database systems and data warehouses, Data warehouses Characteristics, Data warehouse Architecture and its Components, Extraction – Transformation – Loading, Logical (Multi – Dimensional), Data Modeling, Schema Design, Star and Snow – Flake Schema, Fact Consultation, Fact Table, Fully Addictive, Semi – Addictive, Non Addictive Measures; Fact – Less – Facts, Dimension Table Characteristics; OLAP Cube, Olap Operations, OLAP Server Architecture – ROLAP, MOLAP and HOLAP.

UNIT – II

Introducing to Data Mining : Introduction, What is Data Mining, Definition, KDD, Challenges, Data Mining Tasks, Data Preprocessing, Data Cleaning, Missing data, Dimensionality Reduction, Feature Subset Selection, Discretization and Binaryzation, Data Transformation; Measures of Similarity and Dissimilarity – Basics.

UNIT – III

Association Rules : problems Definition, Frequent Item Set Generation, The APRIORI Principle, Support and Confidence Measures, Association Rule Generation; APRIOIRI Algorithm,The Partition Algorithms, FP- Growth Algorithms, Compact Representation of Frequent Ittem set- Maximal Frequent Item Set, Closed Frequent Item Sets.

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TEXT BOOK

• Data Mining – Concepts and Techniques – Jiawei Han, Michelinen Kamber, Morgan Kaufmann Publishers, Elsevier, 2 Edition, 2006.
• Introduction to Data Mining, Pang – Ning Tan, Vipin Kumar, Michael Steinbanch, Pearson Education.

REFERENCE BOOKS

• Data Mining Techniques, Arun K Pujari, 3rd Edition, Universities Press.
• Data Warehouse Fundamentals, Pualraj Ponnaiah, Wiley Student Edition.
• Data Mining, Vikaram Pudi, P Radha Krishna, Oxford University Press

OUTCOMES

• Students should be able to understand why the data warehouse in addition to database systems.
• Ability to perform the preprocessing of data and apply mining techniques on it.
• Ability to identify the association rules, classification and clusters in large data sets.
• Ability to solve real world problems in business and scientific information using data mining.

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