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.

Data Warehousing and Data Mining

Course Title: Data Warehousing and Data Mining

Course No: IT 274

Nature of the Course: Theory + Lab

Semester: 7

Credit Hours: 3

Course Description

This course covers different concepts of data warehousing and data mining including data mining, data warehousing and OLAP, and different concepts and algorithms of pattern mining, classification, clustering and outlier detection.

Course Objectives

The main objective of this course is to provide knowledge of different concepts of data warehousing and data mining to students. After completing this course, students will be able to understand different concepts of data warehousing and data mining, learn to design and use data warehouses and OLAP, know and apply pattern mining algorithms, understand and use classification and clustering algorithms, and learn outlier detection and related algorithms.

Course Contents

1. Introduction

6 hrs

1.1. Data Mining and Knowledge Discovery

1.2. Mining Various Kinds of Knowledge

1.3. Data Mining Applications

1.4. Data Types

1.5. Statistics of Data

1.6. Similarity and Distance Measures

1.7. Data Quality, Data Cleaning, and Data Integration

1.8. Data Transformation

1.9. Dimensionality Reduction

2. Data Warehousing and Online Analytical Processing

8 hrs

2.1. Data Warehouse

2.2. Data Warehouse Modeling: Schema and Measures

2.3. OLAP Operations

2.4. Data Cube Computation

2.5. Data Cube Computation Methods

3. Pattern Mining

5 hrs

3.1. Basic Concepts

3.2. Frequent Itemsets, Closed Itemsets, and Association Rules

3.3. Frequent Itemset Mining Methods: Apriori and Pattern-Growth Approach

3.4. Generating Association Rules from Frequent Itemsets

3.5. Pattern Evaluation Methods

4. Classification

12 hrs

4.1. Basic Concepts

4.2. Decision Tree Induction

4.3. Bayes Classification Methods

4.4. Lazy Learners

4.5. Linear Classifiers

4.6. Model Evaluation and Selection

4.7. Techniques to Improve Classification Accuracy

5. Cluster Analysis

10 hrs

5.1. Cluster Analysis

5.2. Partitioning Methods (k-Means, k-Mediods and k-Modes)

5.3. Hierarchical Methods (Agglomerative and Divisive)

5.4. Desisty-Based (DBSCAN)

5.5. Evaluation of Clustering

6. Outlier Detection

7 hrs

6.1. Basic Concepts

6.2. Statistical Approaches

6.3. Proximity-Based Approaches

6.4. Reconstruction-Based Approaches

6.5. Clustering-Based Approaches

6.6. Classification-Based Approaches

Laboratory Works

The laboratory work includes implementing data warehouse and algorithms for pattern-mining, classification, clustering and outlier detection using any appropriate programming language. Students should also learn to use visualization tools during implementation of these algorithms.

1.Laboratory Work

Reference Books

1.Jiawei Han, Jian Pei, and Hanghang Tong, Data Mining Concepts and Techniques, 4th Edition, Morgan Kaufmann, 2023.

Notes:

This syllabus follows the official BIM curriculum of Tribhuvan University. In case of any doubt or revision, the university’s published syllabus shall be considered authoritative.https://www.fomecd.edu.np/syllabuses

Data Mining

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TabFlux . Data Warehousing and Data Mining . TU . BIM

Data Warehousing and Data Mining

Course Description

Course Objectives

Course Contents

Laboratory Works

Reference Books

Notes:

Select University

Select Program

TabFlux . Data Warehousing and Data Mining . TU . BIM

Data Warehousing and Data Mining

Course Description

Course Objectives

Course Contents

Laboratory Works

Reference Books

Notes: