CSI 661 - Data Mining
MWF 11:15am - 12:05pm @ HU112
Credits: 1-3
Office Hours: Monday, Wednesday 4pm till 5pm
Optional Text: Data Mining: Introduction and Advanced Topics by Margret Dunham
You can buy it through Amazon or Barnes and Noble
Text Book, Topic List and Reading Material List
Assignments
Assignment #1
Vote Data Set - Predict the "class. This data set contains the voting patterns of US representatives on various issues. The Class variable is whether they are democrat or republican." attribute
Pima Indians Data Set - Predict the "class. This data set contains information on Pima Indians and can be used to predict if they have diabetes." attribute
or Assignment #4
Concentration Area
Software WEKA: Does Clustering,
Classification , Ensembles, Association Rules and Regression (Requires JAVA, be sure to download the version that automatically installs
Java if you have Windows). Decision Tree Induction (At the Bottom of the Page): C4.5 Lectures Lecture 1): Class Overview and Logistics Lecture 2,3): Association Rules - Introduction Lecture 4): Sequential Association Rules Lecture 5): Assocation Rules and Bilogical Contact Maps. We will discuss this paper Lecture 6,7,8): Introduction to Classification Lecture 9,10): More Classification Lecture 11): Naive Bayes Classifier Lecture 12-13): Introduction to Clustering Lecture 14-15): More on Clustering Lecture 16-18): Clustering Large Data Bases Lecture 19): Clustering Non-Vector Data Using