Data Warehousing and Data Mining

Course Title: Data Warehousing and Data Mining

Course No: BIT454

Nature of the Course: Theory + Lab

Semester: 8

Full Marks: 60 + 20 + 20

Pass Marks: 24 + 8 + 8

Credit Hours: 3

Course Description

This course introduces advanced aspects of data warehousing and data mining, encompassing the principles, research results and commercial application of the current technologies.

Course Objectives

The main objective of this course is to provide knowledge of different data mining techniques and data warehousing

Course Contents

1. Introduction to Data Warehousing

5 hrs

1.1. Data Warehouse and Data Warehousing

1.2. Differences between Operational Database and Data Warehouse

1.3. MOLAP, OLAP Operations

1.4. Conceptual Modeling of Data Warehouse

1.5. Components of Data Warehouse

2. Introduction to Data Mining

2 hrs

2.1. Motivation for Data Mining

2.2. Introduction to Data Mining System

2.3. Data Mining Functionalities, KDD

2.4. Data Mining Goals

3. Data Preprocessing

3 hrs

3.1. Data Types and Attributes, Various Similarity Measures

3.2. Data Cleaning, Data Integration and Transformation

3.3. Data Reduction

3.4. Data Discretization and Concept Hierarchy Generation

4. Data Cube Technology

4 hrs

4.1. Cube Materialization

Introduction to Full Cube, Iceberg Cube, Closed Cube, Shell Cube

4.2. General Strategies for Cube Computation

4.3. Attribute Oriented Analysis

Attribute Generalization, Attribute Relevance, Class Comparison

5. Mining Frequent Patterns

6 hrs

5.1. Frequent Patterns, Market Basket Analysis, Frequent Itemsets

5.2. Generating Itemsets and Association Rules

5.3. Finding Frequent Itemset

Apriori Algorithm, FP Growth

5.4. Generating Association Rules from Frequent Itemset

5.5. Limitation and Improving Apriori

5.6. Association Mining to Correlation Analysis

5.7. Constraint-Based Association Mining

6. Classification and Prediction

10 hrs

6.1. Definition (Classification, Prediction), Learning and Testing of Classification

6.2. Classification by Decision Tree Induction

ID3 and Gini Index as Attribute Selection Algorithm

6.3. Bayesian Classification, Laplace Smoothing

6.4. Classification by Back Propagation

6.5. Rule Based Classifier, Support Vector Machine

6.6. Associative Classification, Lazy Learners

6.7. Accuracy and Error Measures, Ensemble Methods

6.8. Issues in Classification

7. Cluster Analysis

8 hrs

7.1. Types of Data in Cluster Analysis

7.3. Clustering Techniques

Partitioning Methods, Hierarchical Methods

Density-Based Methods, Grid-Based Methods, Model-Based Clustering Methods

7.4. Clustering High-Dimensional Data

7.5. Constraint-Based Cluster Analysis

7.6. Outlier Analysis

8. Graph Mining and Social Network Analysis

5 hrs

8.1. Graph Mining, Why Graph Mining

8.2. Graph Mining Algorithm

Beam Search, Mining Frequent Sub-Graph

Apriori Graph, Pattern Growth Graph, Graph Indexing

8.3. Social Network Analysis

Characteristics of Social Network, Link Mining

Friends of Friends, Viral Marketing, Community Mining

Theory of Balance, Theory of Status

9. Mining Spatial, Multimedia, Text and Web Data

2 hrs

9.1. Spatial Data Mining, Mining Spatial Association

9.2. Multimedia Data Mining

9.3. An Introduction to Text Mining, Natural Language Processing and Information Extraction

9.4. Web Mining

Web Content Mining, Web Structure Mining, Web Usage Mining

Laboratory Works

The laboratory should contain all the features mentioned in a course, which should include data preprocessing and cleaning, implementing classification, clustering, association algorithms in any programming language, and data visualization through data mining tools.

Text Books

1.Data Mining: Concepts and Techniques, 3rd ed. Jiawei Han, Micheline Kamber, and Jian Pei. Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann Publishers, July 2011
2.Introduction to Data Mining, 2nd ed. Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar. Pearson Publisher, 2019
3.Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman, 2014

Notes:

This syllabus follows the official BIT curriculum of Tribhuvan University. In case of any doubt or revision, the university's published syllabus shall be considered authoritative.

Data Mining