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CS 593 Data Mining II: Advanced Algorithms for Mining Big Data
Syllabus
The syllabus below describes a recent offering of the course, but it may not be completely up to
date. For current details about this course, please contact the course coordinator. Course coordinators
are listed on the course listing for undergraduate courses and graduate courses.
Text Books
Required
[RU] A. Rajaraman, J.D. Ullman , Mining of Massive Data Sets , Cambridge University Press, 2012
[L] D.T. Larose , Data Mining: Methods and Models , Wiley Interscience, 2006
Week-by-Week Schedule
Week Topics Covered
Reading
Assignments
1
Introduction, "Big Data" definition, volume,
velocity and variety
2
Dimensionality reduction techniques
RU chap 1
HW1: use of principal component
3
Dimensionality reduction techniques
RU chap 1
HW 2: Factor Analysis and comparison
with PCA
4
Similarity algorithms
RU chap 3
HW3: practice similarity on a small
dataset
5
Streaming data
RU chap 4
HW4: describe all possible streaming
data types
6
Algorithms for mining streaming data
RU chap 4
HW5: mining and comparing various
streaming algorithms
7
Web mining algorithms
RU chap 8
HW6: application and comparison of web
mining algorithms
8
Online mining
RU chap 8
HW7: numerical comparison of online
algorithms
9
Recommendation system algorithms
RU chap 9
HW8: the Netflix example
10
Recommendation system algorithms
RU chap 9
HW9: small scale Netflix
11
Market basket models
Handout
HW10: the shopping example
12
Naive Bayes and Bayesian networks
L chap 5
HW11: numerical comparison of Naive
Bayes and ANN
13
Naive Bayes and Bayesian networks
L chap 5
HW12: Naive Bayes vs. Classification
14
Summary
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