Introduction to Data Mining

Paper Code: 
DAC 331
Credits: 
02
Periods/week: 
01
Max. Marks: 
100.00
Objective: 

Course Objectives:

The course will enable the students to

1. To study and learn the concepts of Data Mining

2. To develop their skills data mining techniques for data analysis

3. To Implement the qualitative and quantitative data analysis using data mining approaches.

Table 3

 

Course

Learning outcome (at course level)

Learning and teaching strategies

Assessment Strategies

Paper Code

Paper Title

 

DAC331

Introduction to Data Mining

 

Students will :

1)Identify basic applications, concepts, and techniques of data mining.Student will also differentiate supervised and non supervised techniques in data mining.

2)Create association rules and develop tree/graph in market busket 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 accuray,ROC,RMSE,confusion matrix etc.

 5) Generate quantitative analysis reports and perform comparative analysis of algorithms for decision making

Approach in teaching:

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

 

Learning activities for the students:

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

 

Assessment Strategies

Class test, Semester end examinations, Quiz, Solving problems in tutorials, Assignments, Presentation

 

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: 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: 
  • 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: 
2021-22
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