Advanced Data Mining Techniques

Course details

This advanced course delves deeper into data mining, focusing on specialized techniques and modern tools. By blending theory with real-world applications, students will be trained to extract meaningful patterns and insights from vast datasets.

Start Date

TBD

Target Audience

4th & 6th semester undergraduates who've covered foundational data mining courses. Tailored for those targeting advanced analytics roles in industries like finance, retail, and e-commerce.

Duration

14-15 weeks

Learning Objectives

Understand advanced data mining methodologies.
Extract patterns from complex and large datasets.
Apply state-of-the-art data mining tools and platforms.
Address practical business challenges using data mining insights.

Key Features

Comprehensive lectures detailing advanced algorithms and their applications.
Hands-on labs using popular data mining tools and platforms.
Group projects, emphasizing the application of data mining in real-world scenarios.
Continuous assessment through case studies and assignments.
Access to all resources and course materials through the LMS.

Learning path

Unit 1: Review of Basic Data Mining Concepts (4)
Unit 2: Advanced Clustering Techniques (6)
Unit 3: Advanced Classification Techniques (4)
Unit 4: Advanced Association Rule Mining (4)
Unit 5: Anomaly Detection and Outlier Analysis (4)
Unit 6: Advanced Topics (6)

Topics covered

Introduction to Data Mining and its Significance
Data Preprocessing and Transformation
Basic Clustering and Classification Techniques
Association Rule Mining

Hierarchical Clustering
Density-Based Clustering (DBSCAN)
Model-Based Clustering
BIRCH and CHAMELEON
Clustering Validation Techniques
Large Scale Clustering

Support Vector Machines
Ensemble Methods: Boosting, Bagging
Random Forests
Neural Networks and Deep Learning

Frequent Pattern Mining
Maximal and Closed Itemset Mining
Sequential Pattern Mining
Association Rule Pruning

Statistical Methods for Outlier Detection
Distance-Based Outlier Detection
Density-Based Anomaly Detection
Supervised & Semi-Supervised Anomaly Detection

Text Mining and Sentiment Analysis
Web Mining and Social Network Analysis
Time Series and Sequence Data Mining
Big Data Mining Techniques
Stream Data Mining
Ethical Considerations in Data Mining

Grading:

Class Participation: 10%
Bi-weekly Assignments: 30%
Group Project: 20%
Mid-Term Examination: 20%
Final Examination: 20%

Reading Material: Advanced textbooks focusing on deep aspects of data mining, paired with contemporary research papers. All accessible via the LMS.

Assignments: Diverse problems ranging from theoretical understanding to practical data mining challenges.

Faculty: An established academic with a deep understanding and industry experience in advanced data mining techniques.

Teaching Instructors: TBD