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Data Mining

Data mining is the process of discovering hidden patterns, anomalies, and correlations in large datasets using machine learning, statistics, and AI to enable data-driven decisions. It is a critical component of data science, frequently used in fraud detection, marketing, and predictive modeling.

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TabFlux . Data Warehousing and Data Mining . TU . BSC-CSIT

Data Warehousing and Data Mining

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Course Title: Data Warehousing and Data Mining

Course No: CSC420

Nature of the Course: Theory + Lab

Semester: 7

Full Marks: 60 + 20 + 20

Pass Marks: 24 + 8 + 8

Credit Hours: 3

Course Description

Course Objectives

Course Contents

1. Introduction to Data Warehousing

5 hrs

1.1. Lifecycle of data, Types of data, Data warehouse and data warehousing, Differences between operational database and data warehouse

1.2. A multidimensional data model, OLAP operation in multidimensional data model

1.3. Conceptual modeling of data warehouse, Architecture of data warehouse, Data warehouse implementation

1.4. Data marts, Components of data warehouse, Need for data warehousing, Trends in data warehousing

2. Introduction to Data Mining

2 hrs

2.1. Motivation for data mining, Introduction to data mining system, Data mining functionalities

2.2. KDD, Data object and attribute types, Statistical description of data, Issues and Applications

3. Data Preprocessing

3 hrs

3.1. Data cleaning

3.2. Data integration and transformation

3.3. Data reduction

3.4. Data discretization and Concept Hierarchy Generation

3.5. Data mining primitives

4. Data Cube Technology

4 hrs

4.1. Efficient method for data cube computation

4.2. Cube materialization

Full cube

Iceberg cube

Closed cube

Shell cube

4.3. General strategies for cube computation

4.4. Attribute oriented induction for data characterization, Mining class comparison, Discriminating between different classes

5. Mining Frequent Patterns

6 hrs

5.1. Frequent patterns, Market basket analysis, Frequent itemsets, closed itemsets, association rules

5.2. Types of association rule: Single dimensional, multidimensional, multilevel, quantitative

5.3. Finding frequent itemset

Apriori algorithm

FP growth

5.4. Generating association rules from frequent itemset, Limitation and improving Apriori

5.5. From Association Mining to Correlation Analysis, Lift

6. Classification and Prediction

10 hrs

6.1. Definition (Classification, Prediction), Learning and testing of classification

6.2. Classification by decision tree induction, ID3 as attribute selection algorithm

6.3. Bayesian classification, Laplace smoothing

6.4. Classification by backpropagation

6.5. Rule based classifier: Decision tree to rules, rule coverage and accuracy, efficient of rule simplification

6.6. Support vector machine

6.7. Evaluating accuracy: precision, recall, f-measure

6.8. Issues in classification, Overfitting and underfitting, K-fold cross validation

6.9. Comparing two classifier (McNemar's test)

7. Cluster Analysis

8 hrs

7.1. Types of data in cluster analysis, Similarity and dissimilarity between objects

7.2. Clustering techniques

Partitioning: k-means, k-means++, Mini-Batch k-means, k-medoids

Hierarchical: Agglomerative and Divisive

Density based: DBSCAN

7.3. Outlier analysis

8. Graph Mining and Social Network Analysis

5 hrs

8.1. Graph mining, Why graph mining, Graph mining algorithm: Beam search, Inductive logic programming

8.2. Social network analysis, Link mining, Friends of friends, Degree assortativity

8.3. Signed network

Theory of structured balance

Theory of status

Conflict between the theory of balance and status

8.4. Trust in a network

Atomic propagation

Propagation of distrust

Iterative propagation

8.5. Predicting positive and negative links

9. Mining Spatial, Multimedia, Text and Web Data

2 hrs

9.1. Spatial data mining, Spatial data cube, Mining spatial association

9.2. Multimedia data mining, Similarity search in multimedia data, Mining association in multimedia data

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

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.

Reference Books

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

Notes:

Source:

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

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

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.

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