JNTUH B.Tech R18 Data Mining Study Material / Notes - Set 1
Unit -1 : Data Mining
Unit -2 : Association Rule Mining
Unit -3 : Classification
Unit -4 : Clustering and Applications
Unit -5 : Advanced Concepts
JNTUH B.Tech R18 Data Mining Study Material / Notes - Set 2
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JNTUH B.Tech R18 Data Mining Short and Long Important Questions and Objective Bits - Unit Wise
www.forum.universityupdates.in
JNTUH B.Tech R18 Data Mining Syllabus :
Unit 1 :
Data Mining: Data–Types of Data–, Data Mining Functionalities– Interestingness Patterns Classification of Data Mining systems– Data mining Task primitives –Integration of Data mining system with a Data warehouse–Major issues in Data Mining–Data Preprocessing.
Unit 2 :
Association Rule Mining: Mining Frequent Patterns–Associations and correlations – Mining Methods– Mining Various kinds of Association Rules– Correlation Analysis– Constraint based Association mining. Graph Pattern Mining, SPM.
Unit 3 :
Classification: Classification and Prediction – Basic concepts–Decision tree induction–Bayesian classification, Rule–based classification, Lazy learner.
Unit 4 :
Clustering and Applications: Cluster analysis–Types of Data in Cluster Analysis–Categorization of Major Clustering Methods– Partitioning Methods, Hierarchical Methods– Density–Based Methods, Grid–Based Methods, Outlier Analysis.
Unit 5 :
Advanced Concepts: Basic concepts in Mining data streams–Mining Time–series data––Mining sequence patterns in Transactional databases– Mining Object– Spatial– Multimedia–Text and Web data – Spatial Data mining– Multimedia Data mining–Text Mining– Mining the World Wide Web.
Unit -1 : Data Mining
Unit -2 : Association Rule Mining
Unit -3 : Classification
Unit -4 : Clustering and Applications
Unit -5 : Advanced Concepts
JNTUH B.Tech R18 Data Mining Study Material / Notes - Set 2
Download Here
JNTUH B.Tech R18 Data Mining Short and Long Important Questions and Objective Bits - Unit Wise
Download attachment
www.forum.universityupdates.in
JNTUH B.Tech R18 Data Mining Syllabus :
Unit 1 :
Data Mining: Data–Types of Data–, Data Mining Functionalities– Interestingness Patterns Classification of Data Mining systems– Data mining Task primitives –Integration of Data mining system with a Data warehouse–Major issues in Data Mining–Data Preprocessing.
Unit 2 :
Association Rule Mining: Mining Frequent Patterns–Associations and correlations – Mining Methods– Mining Various kinds of Association Rules– Correlation Analysis– Constraint based Association mining. Graph Pattern Mining, SPM.
Unit 3 :
Classification: Classification and Prediction – Basic concepts–Decision tree induction–Bayesian classification, Rule–based classification, Lazy learner.
Unit 4 :
Clustering and Applications: Cluster analysis–Types of Data in Cluster Analysis–Categorization of Major Clustering Methods– Partitioning Methods, Hierarchical Methods– Density–Based Methods, Grid–Based Methods, Outlier Analysis.
Unit 5 :
Advanced Concepts: Basic concepts in Mining data streams–Mining Time–series data––Mining sequence patterns in Transactional databases– Mining Object– Spatial– Multimedia–Text and Web data – Spatial Data mining– Multimedia Data mining–Text Mining– Mining the World Wide Web.
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