Data Warehousing and Data Mining Syllabus (CSC 410) – TU BSc CSIT 7th Semester

Tribhuvan University
Institute of Science and Technology
Bachelor of Science in Computer Science and Information Technology

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

Course Contents

Unit 1. Introduction to Data Warehousing (5 Hrs.)

Lifecycle of Data, Types of Data, Data Warehouse and Data Warehousing, Differences between Operational Database and Data Warehouse, A Multidimensional Data Model, OLAP Operations in Multidimensional Data Model, Conceptual Modeling of Data Warehouse, Architecture of Data Warehouse, Data Warehouse Implementation, Data Marts, Components of Data Warehouse, Need for Data Warehousing, Trends in Data Warehousing

Unit 2. Introduction to Data Mining (2 Hrs.)

Motivation for Data Mining, Introduction to Data Mining System, Data Mining Functionalities, KDD, Data Object and Attribute Types, Statistical Description of Data, Issues and Applications

Unit 3. Data Preprocessing (3 Hrs.)

Data Cleaning, Data Integration and Transformation, Data Reduction, Data Discretization and Concept Hierarchy Generation, Data Mining Primitives

Unit 4. Data Cube Technology (4 Hrs.)

Efficient Methods for Data Cube Computation, Cube Materialization (Introduction to Full Cube, Iceberg Cube, Closed Cube, Shell Cube), General Strategies for Cube Computation, Attribute Oriented Induction for Data Characterization, Mining Class Comparison, Discriminating between Different Classes

Unit 5. Mining Frequent Patterns (6 Hrs.)

Frequent Patterns, Market Basket Analysis, Frequent Itemsets, Closed Itemsets, Association Rules, Types of Association Rule (Single-dimensional, Multidimensional, Multilevel, Quantitative), Finding Frequent Itemsets (Apriori Algorithm, FP Growth), Generating Association Rules from Frequent Itemsets, Limitations and Improving Apriori, From Association Mining to Correlation Analysis, Lift

Unit 6. Classification and Prediction (10 Hrs.)

Definition (Classification, Prediction), Learning and Testing of Classification, Classification by Decision Tree Induction, ID3 as Attribute Selection Algorithm, Bayesian Classification, Laplace Smoothing, Classification by Backpropagation, Rule Based Classifier (Decision Tree to Rules, Rule Coverage and Accuracy, Efficiency of Rule Simplification), Support Vector Machine, Evaluating Accuracy (Precision, Recall, F-measure), Issues in Classification, Overfitting and Underfitting, K-fold Cross Validation, Comparing Two Classifiers (McNemar’s Test)

Unit 7. Cluster Analysis (8 Hrs.)

Types of Data in Cluster Analysis, Similarity and Dissimilarity between Objects, Clustering Techniques: Partitioning (k-means, k-means++, Mini-Batch k-means, k-medoids), Hierarchical (Agglomerative and Divisive), Density Based (DBSCAN), Outlier Analysis

Unit 8. Graph Mining and Social Network Analysis (5 Hrs.)

Graph Mining, Why Graph Mining, Graph Mining Algorithms (Beam Search, Inductive Logic Programming), Social Network Analysis, Link Mining, Friends of Friends, Degree Assortativity, Signed Network (Theory of Structural Balance, Theory of Status, Conflict between the Theory of Balance and Status), Trust in a Network (Atomic Propagation, Propagation of Distrust, Iterative Propagation), Predicting Positive and Negative Links

Unit 9. Mining Spatial, Multimedia, Text and Web Data (2 Hrs.)

Spatial Data Mining, Spatial Data Cube, Mining Spatial Association, Multimedia Data Mining, Similarity Search in Multimedia Data, Mining Association in Multimedia Data, An Introduction to Text Mining, Natural Language Processing and Information Extraction, Web Mining (Web Content Mining, Web Structure Mining, Web Usage Mining)

Laboratory Works

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

Text Books

1. Jiawei Han, Micheline Kamber, and Jian Pei, “Data Mining: Concepts and Techniques”, 3rd Edition, Morgan Kaufmann Publishers, July 2011

Reference Books

1. Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar, “Introduction to Data Mining”, 2nd Edition, Pearson, 2019
2. Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman, “Mining of Massive Datasets”, 2014