{"id":1873,"date":"2016-06-28T17:57:16","date_gmt":"2016-06-28T17:57:16","guid":{"rendered":"http:\/\/www.inspirenignite.com\/jntuh\/?p=1873"},"modified":"2021-10-27T20:41:59","modified_gmt":"2021-10-27T20:41:59","slug":"jntuh-b-tech-4th-year-1-sem-computer-science-and-engineering-r13-4-1-data-warehousing-and-data-mining-r13-syllabus","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/jntuh\/jntuh-b-tech-4th-year-1-sem-computer-science-and-engineering-r13-4-1-data-warehousing-and-data-mining-r13-syllabus\/","title":{"rendered":"JNTUH B.Tech 4th Year 1 sem Computer Science and Engineering R13 (4-1) Data Warehousing and Data Mining R13 syllabus."},"content":{"rendered":"

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.<\/p>\n

Objectives<\/strong><\/p>\n

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.<\/p>\n

UNIT – I<\/strong><\/p>\n

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.<\/p>\n

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.<\/p>\n

UNIT – II<\/strong><\/p>\n

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.<\/p>\n

UNIT – III<\/strong><\/p>\n

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.<\/p>\n

Download iStudy Android App for complete JNTUH syllabus, results, timetables and all other updates. There are no ads and no pdfs and will make your life way easier<\/span>.<\/a><\/strong><\/p>\n

TEXT BOOK<\/strong><\/p>\n