Course Syllabus

Course Description

The data mining pipeline: data collection, data preprocessing, data analysis, data post processing. Supervised learning and unsupervised learning strategies. Classification, clustering, frequent itemset and association rule mining, link analysis, dimensionality reduction, visualization. Data mining in time series, text and web mining

Course Objectives

This course intends to provide a solid understanding and the ability to programmatically apply the fundamental concepts of data mining, including clustering, classification, frequent itemset mining, link analysis etc. Special attention is paid towards the preprocessing and postprocessing stages, in order to provide a complete journey on the data mining pipeline.

Key Concepts

1. Data mining pipeline.
2. Data preprocessing.
3. Clustering.
4. Classification.
5. Link analysis (mining graph data)
6. Data post-processing

Learning Outcomes

1. An understanding of the data mining pipeline: data collection, preprocessing, analysis and postprocessing
2. To understand, apply and interpret the results of clustering algorithms
3. To understand, apply and interpret the results of classification algorithms
4. To understand, apply and interpret the results of frequent itemset mining algorithms
5. To understand, apply and interpret the results of link analysis algorithms
6. To implement various data post-processing techniques

Recommended Textbooks

"Introduction to Data Mining" (Global Edition), by Tan, Steinbach, Karpatne, Kumar (Pearson 2019) 2nd edition