COMP 465
Topics in Computer Science: Data Mining
(CRN 25284, Spring 2015)

Announcements

  • You should install WEKA (Stable Book 3rd ed. version) by Thursday, February 5th.
  • The first lecture will be held on January 15, 2015.

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    Course Description

    Data mining, or intelligent analysis of information stored in data sets, has recently gained a substantial interest among practitioners in a variety of fields and industries. This course will introduce the process of knowledge discovery and the basic theory of automatically extracting models from data, validating those models, solving the problems of how to extract valid, useful, and previously unknown interesting patterns from a source (database or web) which contains an overwhelming amount of information. Students will be introduced to various models (decision trees, association rules, linear model, clustering, Bayesian network, neural network) and learn how to apply them in practice. Algorithms applied include searching for patterns in the data, using machine learning, and applying artificial intelligence techniques. Students will learn how to implement several relevant algorithms and use existing tools to mine real-world data.

    The syllabus for this course can be found at http://cs.rhodes.edu/welshc/COMP465_S15/syllabus.pdf.

    Course Information and Prerequisites

    Location: Ohlendorf 225
    Time: TTh 9:30-10:45PM
    URL: http://cs.rhodes.edu/welshc/COMP465_S15/
    Prerequisite: COMP 241


    Course Instructor

    Instructor: Catie Welsh
    Office: Ohlendorf 422
    Email: welshc@rhodes.edu (please include "CS 465" somewhere in the subject)
    Office Hours: Tues and Thurs 2-4pm, Ohlendorf 422







    Schedule

    This is a tentative schedule and subject to change as needed.

    Date Lecture Topic(s) Reading Assignment Homework
    Th-January 15 Introduction slides Chapter 1 - Han Book
    T-January 20 Getting to Know your Data slides Chapter 2 - Han Book Sign-Up for Paper Presentations (Sign-up link will be emailed to you this afternoon)
    Th-January 22 Data Preprocessing slides Chapter 3 - Han Book  
    T-January 27 More Data Prepropressing slides  
    Th-January 29 Data Warehousing and OLAP slides Chapter 4 Install WEKA by 2/5
    T-February 3 Mining Frequent Patterns slides Chapter 6  
    Th-February 5 Introduction to WEKA - Tutorial    
    T-February 10 Mining Frequent Pattern Algorithms    
    Th-February 12 Intro to Classification slides Section 8.1-8.2 Group Project Information
    T-February 17 Snow Day
    Th-February 19 More Classification slides Section 8.3-8.4  
    T-February 24 Still More Classification slides Section 8.5-8.7  
    Th-February 26 Classification Activity   Group Project Proposals due colic.arff zoo.arff
    T-March 3 Review    
    Th-March 5 Midterm
    T-March 10 Spring Break - No Class
    Th-March 12 Spring Break - No Class
    T- March 17 Clustering Basics slides Section 10.1-10.3  
    Th-March 19 More on Clustering slides Section 10.4  
    T-March 24 Even More on Clustering slides Section 10.5-10.7  
    Th-March 26 Clustering with WEKA    
    T-March 31 Clustering Activity    
    Th-April 2 Easter Break - No Class
    T-April 7 Work Period MMDS Chapter 2 Watch Video Lectures & Take Moodle quiz
    Th-April 9 Link Analysis & PageRank slides MMDS Chapter 5 Project Checkpoint Paper due
    T-April 14 More on PageRank slides    
    Th-April 16 PageRank on Large Graphs slides    
    T-April 21 Recommender Systems slides MMDS Chapter 9  
    Th-April 23 More on Recommender Systems slides    
    T-April 28 Project Presentations: Groups Below
    James, Andrew, David, & Alex W.
    Alex H, Daniel, John, Matt
    Wyatt P, Lucas, Kris, Khang
    Preston, Wyatt G, Joel, Katie
    		   
    Th-April 30 Project Presentations: Groups Below
    Farah, Trevor, Jack, Crawford
    Casey, CG, Corrie, Haley
    Hong, Josh, Will, Connor
    Evan, Max, Sumner, Morgan
    		  Final Project Paper due
    Tuesday-May 5 Final Exam, 8:30-11am