{"id":8161,"date":"2019-12-15T03:46:11","date_gmt":"2019-12-15T03:46:11","guid":{"rendered":"https:\/\/www.inspirenignite.com\/vtu\/data-mining-and-data-warehousing-is-6th-sem-syllabus-for-vtu-be-2017-scheme-professional-elective-2\/"},"modified":"2019-12-15T03:46:11","modified_gmt":"2019-12-15T03:46:11","slug":"data-mining-and-data-warehousing-is-6th-sem-syllabus-for-vtu-be-2017-scheme-professional-elective-2","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/vtu\/data-mining-and-data-warehousing-is-6th-sem-syllabus-for-vtu-be-2017-scheme-professional-elective-2\/","title":{"rendered":"Data Mining and Data Warehousing IS 6th Sem Syllabus for VTU BE 2017 Scheme (Professional Elective-2)"},"content":{"rendered":"

Data Mining and Data Warehousing detail syllabus for Information Science Engineering (IS), 2017 scheme is taken from VTU<\/a> official website and presented for VTU students. The course code (17CS651), and for exam duration, Teaching Hr\/week, Practical Hr\/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.<\/p>\n

For all other is 6th sem syllabus for be 2017 scheme vtu you can visit IS 6th Sem syllabus for BE 2017 Scheme VTU Subjects<\/a>. For all other Professional Elective-2 subjects do refer to Professional Elective-2<\/a>. The detail syllabus for data mining and data warehousing is as follows.<\/p>\n

Module 1\t\t\t\t<\/h4>\n

Data Warehousing&modeling: Basic Concepts: Data Warehousing: A multitier Architecture, Data warehouse models: Enterprise warehouse, Data mart and virtual warehouse, Extraction, Transformation and loading, Data Cube: A multidimensional data model, Stars, Snowflakes and Fact constellations: Schemas for multidimensional Data models, Dimensions: The role of concept Hierarchies, Measures: Their Categorization and computation, Typical OLAP Operations.\n<\/p>\n

Module 2
\nFor complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Module 3
\n<\/h4>\n

Association Analysis: Association Analysis: Problem Definition, Frequent Item set Generation, Rule generation. Alternative Methods for Generating Frequent Item sets, FP-Growth Algorithm, Evaluation of Association Patterns.\n<\/p>\n

Module 4
\n<\/h4>\n

Classification :Decision Trees Induction,Method for Comparing Classifiers, Rule Based Classifiers, Nearest Neighbor Classifiers,Bayesian Classifiers.\n<\/p>\n

Module 5
\nFor complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Course Outcomes:<\/h4>\n

The students should be able to:\t\t\t\t<\/p>\n