Introduction to Data Mining (CSE 304) 

Syllabus

This course, 'Introduction to Data Mining,' spans 16 weeks and is aimed at providing students with a comprehensive understanding of how to extract valuable insights from large datasets. It covers a range of topics, from fundamental concepts to advanced data mining techniques. Students will learn to identify patterns, analyze real-time data streams, understand link analysis, and apply clustering techniques. The course also delves into specific applications like web advertising strategies, recommendation systems, and social network analysis. Designed for individuals with a basic background in math and programming, the course combines theoretical knowledge with practical assignments, preparing students to tackle real-world data mining challenges effectively

2024S courses (TA: Song Kim, Dahee Kim, Hyewon Kim)

Midterm (statistics),  Final (statistics)

Assignment Manual, Homework #1, Homework #2

Tentative Outlines for the Courses

• Introduction to Data Mining

  • Readings: 

    1. 1

• Data

  • Readings: 

    1. 1

• Frequent pattern mining

  • Readings:

    1. 1

• Classification

  • Readings:

    1. 1

• Clustering

  • Readings:

    1. 1

• Hashing

  • Readings:

    1. 1

• Social network analysis

  • Readings:

    1. 1

• Link analysis

  • Readings:

    1. 1

• Stream data analysis

  • Readings:

    1. 1

Recitation

• Java and Eclipse tutorial -.

  • References:

    1. 11

• Overleaf tutorial - .

  • References:

    1. 11

• Summary of data structure & algorithms - Overview of fundamental data structures, their properties, and applications.

  • References:

    1. Data structures & Algorithms in Java - Ch 1-14

• Summary of probability, statistics, and linear algebra - Introduction to basic probability and statistics that are utilised in data mining course. 

  • References:

    1. -

• Polynomial time reduction & NP-hardness - Concepts of polynomial-time reduction, NP-completeness, and NP-hardness.

  • Readings:

    1. Introduction to Algorithms - Ch 34 

    2. Algorithm Design - Ch 8

• Approximation algorithm - Study of approximation algorithms for NP-hard problems, PTAS, FPTAS, and their performance guarantees. 

  • Readings:

    1. Introduction to Algorithms - Ch 35 

    2. Algorithm Design - Ch 11

• Flow - Understanding flow in networks and related algorithms.

  • Readings:

    1. Algorithm Design - Ch 7

  • References: 

    1. The Algorithm Design Manual - Ch 8

    2. Introduction to Algorithms - Ch 24

    3. Algorithms by Jeff Erickson - Ch 10

Tutorials

• LaTeX: http://www.ctan.org/tex-archive/info/lshort/english/lshort.pdf

• Overleaf: https://www.overleaf.com/learn/latex/Tutorials

• Overleaf: https://ko.overleaf.com/learn/latex/Learn_LaTeX_in_30_minutes

• Install Eclipse: https://www.educative.io/answers/how-to-install-eclipse-ide

• Install Java 11: https://drive.google.com/file/d/1J7M4Aud_7MocSNa6qAaq7dsHQPkmALdO/view?usp=drive_link

With highest honor

• 2024S: Donggyu Lee

References

• Data Mining: Concepts and Techniques

• Introduction to Data Mining

• MMDS: http://www.mmds.org/

• Videos: https://www.coursera.org/specializations/data-mining