Introduction to Data Mining

Paper Code: 
25DAC331
Credits: 
2
Periods/week: 
1
Max. Marks: 
100.00
Objective: 

This course will enable students to learn basic data mining concepts. This will help them in understanding analytical procedures used in Business Analytics through data mining approach.

Course Outcomes: 

Learning outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

  1. Identify basic applications,   concepts, and techniques of data mining.
  2.  Create association rules and develop tree/graph in market basket dataset using Apriori and FP tree algorithms
  3.  Analyze large datasets to gain business understanding and apply classification, prediction and clustering algorithms.
  4. Evaluate classification/prediction models using metrics like accuracy, ROC, RMSE, confusion matrix etc.
  5. Create quantitative analysis reports and perform comparative analysis of algorithms for decision making
  6. Contribute effectively in course-specific interaction

Approach in teaching:

Interactive Lectures, Discussion, Demonstrations, Group activities, Teaching using advanced IT audio-video tools. 

Learning activities for the students:

Self-learning assignments, Effective questions, Seminar presentation, Giving tasks.

Two Internal Tests, Quiz, Home Assignments, Presentations.

 

 

6.00
Unit I: 
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:

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:

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 and ANN:

 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:

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

ESSENTIAL READINGS: 
  1. Jiawei Han & Micheline Kamber, “Data Mining: Concepts & Techniques”, Morgan Kaufmann Publishers, Third Edition.
  2. Mohanty, Soumendra, “Data Warehousing: Design, Development and Best Practices”, Tata McGraw Hill, 2006
REFERENCES: 

1.       W. H. Inmon, “Building the Data Warehouse”, Wiley Dreamtech India Pvt. Ltd., 4th  Edition, 2005

 

e-Resources:

1.       https://nptel.ac.in/courses/106106222

2.       www.kaggle.com

Journals:

1.      International Journal of Design

2.      Journal of the Brazilian Computer Society, SpringerOpen

Journal of Internet Services and Applications, SpringerOpen

Academic Year: 
2025-26
  • Printer-friendly version