Rajiv Gandhi Technological University, Bhopal (MP)
M.Tech. Sofware Engineering
Data Mining Syllabus
Enforced From Session 2009-10 SECOND SEMESTER (PROPOSED)
M.Tech. Sofware Engineering
Data Mining Syllabus
Enforced From Session 2009-10 SECOND SEMESTER (PROPOSED)
UNIT-1: Introduction of Data Mining, Data, Information and Knowledge Discovery, Data Mining Functionalities, Data Mining System categorization and its Issues. Data Processing: - Data Cleaning, Data Integration and Transformation. Data Reduction, Data Mining Statistics. Guidelines for Successful Data Mining, Data Mining Software.
UNIT-2: Classification:-Introduction, Decision Tree, Algorithms for Decision Tree Induction, Characteristics of Decision Tree Induction, Evaluation the Performance of a Classifier:-Holdout Method, Random Sub sampling, Cross-Validation. Rule Based Classifier, Nearest –Neighbors Classifiers, Bayesian Classifiers, Support Vector Machine (SVM)
UNIT-3: Cluster Analysis:- Introduction, Desired Features of Cluster Analysis, Cluster Evaluation, Agglomerative Hierarchical Clustering, Prototype-Based Clustering, Density-Based Clustering, Graph based Clustering, Scalable Clustering Algorithms
UNIT-4: Association Rule Mining:-Introduction, Basic, The Task and a Naïve Algorithm, Apriori Algorithms, Improving the efficiency of the Apriori Algorithm, Apriori-Tid, Direct Hasing and Pruning(DHP),Dynamic Itemset Counting (DIC), Mining Frequent Patterns without Candidate Generation(FP-Growth),Performance Evaluation of Algorithms, Software for Association Rule Mining
UNIT-5: Spatial Data Mining, Multimedia Data Mining, Text Ming, Web Data Mining.
UNIT-2: Classification:-Introduction, Decision Tree, Algorithms for Decision Tree Induction, Characteristics of Decision Tree Induction, Evaluation the Performance of a Classifier:-Holdout Method, Random Sub sampling, Cross-Validation. Rule Based Classifier, Nearest –Neighbors Classifiers, Bayesian Classifiers, Support Vector Machine (SVM)
UNIT-3: Cluster Analysis:- Introduction, Desired Features of Cluster Analysis, Cluster Evaluation, Agglomerative Hierarchical Clustering, Prototype-Based Clustering, Density-Based Clustering, Graph based Clustering, Scalable Clustering Algorithms
UNIT-4: Association Rule Mining:-Introduction, Basic, The Task and a Naïve Algorithm, Apriori Algorithms, Improving the efficiency of the Apriori Algorithm, Apriori-Tid, Direct Hasing and Pruning(DHP),Dynamic Itemset Counting (DIC), Mining Frequent Patterns without Candidate Generation(FP-Growth),Performance Evaluation of Algorithms, Software for Association Rule Mining
UNIT-5: Spatial Data Mining, Multimedia Data Mining, Text Ming, Web Data Mining.
References:
1. Jiawei Han and Micheline Kamber, Data Mining Concepts & Techniques, Elsevier Pub.
2. Tan,Steinbach,Kumar, Introduction To Data Mining,Pearson Education.
3. Arun.K.Pujari, Data Mining Techniques.
4. N.P Gopalan: Data Mining Technique & Trend, PHI
5. Ali: Data Mining:Methods and Techniques, Cenage Learning
6. Hand, Mannila & Smith: Principle of Data Mining, PHI
7. Vikram Pudi: Data Mining, Oxford Univ Press
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