CSCI 4390/6390 Database Mining
This course will provide an introductory survey of the main topics in data mining and knowledge discovery in databases (KDD), including:
frequent pattern mining, sequence mining, graph pattern mining, dimensionality reduction, kernel methods, clustering, classification,
similarity search, recommender systems, etc. Emphasis will be on the algorithmic and system issues in KDD, as well as on practical
applications such as Web mining, multimedia mining,
CSCI 2300 and MATH 2800. You should be familiar with calculus,
linear algebra, probability and statistics, and algorithms/programming.
Data Mining and Analysis, M. J. Zaki and W. Meira, 2014. http://www.dataminingbook.info/pmwiki.php
50% assignment + 50% project. 6 assignments (choosing 5 best scores to count in) and 3 projects.
1st week. Introduction to data mining. Lecture 1 Lecture 2
3rd week. Convex optimization, probability, and graph pattern mining. Lecture 5 Lecture 6
4th week. Random walks on graphs I and large graph mining I. Lecture 7 Lecture 8 Assignment 2
5th week. Random walks on graphs II and large graph mining II. Lecture 9
7th week. Itemset mining, sequence mining, and time series analysis. Lecture 11 Lecture 12
8th week. Dimensionality reduction I. Lecture 13 Part I
9th week. Dimensionality reduction II. Lecture 13 Part II
10th week. Project presentations, and kernel methods.
11th week. Clustering I and II. Lecture 14 Lecture 15
13th week. Classification III, and recommender systems I. Assignment 3 Lecture 18 Lecture 19
14th week. Recommender systems II. Lecture 20 Assignment 4
15th week. Project presentations, and course summary. Project 3 (free choice)