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, bioinformatics, etc.

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.


       Wei Liu, Ph.D.


Hao Li   TA hours: Thurs 9am-10am, Amos Eaton 217. 

Grading Policy

       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


        2nd week.    Linear algebra, probability and statistics.  Lecture 3  Lecture 4  Assignment 1


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 


6th  week.   Project discussion, and large graph mining III.   Lecture 10  Project 1  Prob1_data Prob2_data


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


12th  week.   Classification I and II, and project discussion.   Lecture 16  Lecture 17   Project 2 Prob1_data Prob2_data


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)