Introduction to Data Mining

Paper Code: 
BAC 331
Credits: 
2
Contact Hours: 
30.00
Max. Marks: 
100.00
Objective: 

Students will learn basic data mining concepts .this will help them in understanding analytical procedures used in Business Analytics through data mining approach. 

6.00
Unit I: 
Introduction to Data Warehousing

Introduction to Data Warehousing: Architecture of Data Warehouse, Data Preprocessing – Need, Data Cleaning, Data Integration &Transformation, Data Reduction, Machine Learning, Pattern Matching. Introduction to Data Mining: Basic Data Mining Tasks, Data Mining versus Knowledge Discovery in Databases, Data Mining Metrics, Data Mining Query Language, Applications of Data Mining.

6.00
Unit II: 
Data Mining Techniques

Data Mining Techniques: Frequent item-sets and Association rule mining: Apriori algorithm, Use of sampling for frequent item-set, FP tree algorithm, Graph Mining, Frequent sub-graph mining. Market Basket Analysis and Association Analysis, Market Basket Data, Stores, Customers, Orders, Items, Order Characteristics, Product Popularity, Tracking Marketing Interventions.

6.00
Unit III: 
Classification & Prediction

Classification & Prediction: Decision tree learning: Construction, performance, attribute selection Issues: Over-fitting, tree pruning methods, missing values, Information Gain, Gain Ratio, Gini Index, continuous classes. Classification and Regression Trees (CART) and C 5.0 .

6.00
Unit IV: 
Bayesian Classification

Bayesian Classification: Bayes Theorem, Naïve Bayes classifier, Bayesian Networks Inference, Parameter and structure learning: Linear classifiers, Least squares, logistic, perceptron and SVM classifiers, Prediction: Linear regression, Non-linear regression (Artificial Neural Networks).

6.00
Unit V: 
Accuracy Measures

Accuracy Measures: Precision, recall, F-measure, confusion matrix, cross-validation, bootstrap, Clustering: k-means, Expectation Maximization (M) algorithm, Hierarchical clustering, Correlation clustering, DBSCAN.

Essential Readings: 
  • Jiawei Han & Micheline Kamber, “Data Mining: Concepts & Techniques”, Morgan Kaufmann Publishers, Third Edition.
  • Mohanty, Soumendra, “Data Warehousing: Design, Development and Best Practices”, Tata McGraw Hill, 2006
  • W. H. Inmon, “Building the Data Warehouse”, Wiley Dreamtech India Pvt. Ltd., 4th  Edition, 2005
Academic Year: