CS 631H Data Mining

  • Instructor: Prof. Sung-Hyuk Cha

  • CRN: 51897

  • Meeting:
    • Meeting Times: WWW
    • Place: WWW

  • Textbook: Ian H. Witten and Eibe Frank, Data Mining Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, (1999)

  • Description:
    This course will provide an overview of topics such as introduction to data mining and knowledge discovery; data mining with structured and unstructured data; foundations of pattern clustering; clustering paradigms; clustering for data mining; data mining using neural networks and genetic algorithms; fast discovery of association rules; applications of data mining to pattern classification; and feature selection.

    The goal of this course is to introduce students to current machine learning and related data mining methods. It is intended to provide enough background to allow students to apply machine learning and data mining techniques to learning problems in a variety of application areas. Course projects will be required.

  • Prerequisites: None

  • Lecture Notes: can be accessed using the http://blackboard.pace.edu
    Blackboard Login Procedures for Registered Students are available here

  • Useful Links: click here

  • Project: click here.

  • Tentative Schedule:

    Week Topic
    1 (7/12) Introduction
    2 (7/14) 2. Input: Concepts, instances, attributes
    3 (7/19) 3 Output: Knowledge representation
    4 (7/21) Bayesian Learning
    5 (7/26) Decision Tree
    6 (7/28) Instance based Learning
    7 (8/2) Association Rule Mining
    8 (8/4) Aritificial Neural Network
    9 (8/9) Genetic Algorithms
    10 (8/11) Clustering
    11 (8/16) Support Vector Machine
    12 Project presentation & Demo

  • Evaluation:
    • Project (50%): Students are required to implement one computer vision application (Presentation and report required.)
    • Homeworks (50%): There will be five homeworks.