Laboratory Proficiency I (LP I) is the companion course of theory courses (core and elective) in
Semester I. Continuous assessment of laboratory work is done based on performance of student.
06 December 2017
04 December 2017
ELE-1 DATA MINING [510105B ] ME COMPUTER ENGINEERING 2017 PAT
Compulsory
Module -1 , Introduction to D.M. , Credit – 1
Syllabus
:- Data, Information and Knowledge, Attribute Types: Nominal,
Binary, Ordinal and Numeric attributes, Discrete versus Continuous
Attributes, Introduction to Data Preprocessing, Data Cleaning, Data
integration, data reduction, transformation and Data Descritization.
Concept of class: Characterization and Discrimination, basics
/Introduction to: Classification and Regression for Predictive
Analysis, Mining Frequent Patterns, Associations, and Correlations,
Cluster Analysis
Reference
Book :- Han, Jiawei Kamber,
Micheline Pei and Jian, “Data Mining: Concepts and Techniques”
Elsevier Publishers Third Edition/Second Edition, ISBN:
9780123814791, 9780123814807
Optional
Module No 4 , Classification , Credit 2
Syllabus:-
Basic Concepts, General
Approach to Classification, Decision Tree Induction, Attribute
Selection Measures, Tree Pruning, Scalability and Decision Tree
Induction, Visual Mining for Decision Tree Induction, Bayes
Classification Methods, Baye’s Theorem, Naive Bayesian
Classification, Rule-Based Classification, Using IF-THEN Rules for
Classification, Rule Extraction from a Decision Tree, Rule Induction
Using a Sequential Covering Algorithm, Model Evaluation and
Selection: Metrics for Evaluating Classifier Performance, Holdout
Method and Random Sub sampling, Cross-Validation, Bootstrap, Model
Selection Using Statistical Tests of Significance, Comparing
Classifiers Based on Cost–Benefit and ROC Curves, Techniques to
Improve Classification Accuracy: Introducing Ensemble Methods,
Bagging, Boosting and Ada Boost, Random Forests, Improving
Classification Accuracy of Class-Imbalanced Data.
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