FRE 7851 Topics in Financial & Risk Engineering: Data Mining

desertcockatooData Management

Nov 20, 2013 (3 years and 6 months ago)

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FRE 7851 Topics in Financial & Risk Engineering
: Dat
a Mining


Lecture periods:

2.5 hours

(for 7 weeks)

Laboratory periods:

0 hours

Recitation periods:


0 hours

Credits:



1.5


Dat
a

are accumulating at incredible rate in almost every sector of our life due
to
technological advances in areas such as the internet, wireless telecommunication, point
-
of
-
sale devices, and data storage.
A wealth of useful information is hidden

in this vast
amount of data. Nuggets of m
eaningful
correlations, patterns and trends
ca
n be
discovered using a variety of techniques in Data Mining to

sifting through large amounts

of data stored in repositories

and data warehouses. Some proven successful applications
of data mining in finance include

forecasting stock market, currency exch
ange rate, bank
bankruptcies, understanding

and managing financial risk, trading futures, credit rating,
loan

management, bank customer profiling
, and money laundering analyses.


Data mining t
echniques
covered in this course
may
include, for example, k
-
Nea
rest
Neighbor algorithm
s, Classification and Regression Trees, Discrimination Analysis,
Logistic Regression, Artificial Neural Networks, Multiple Linear Regression, k
-
Means
Clustering, Hierarchical Clustering, Principal Components Analysis, Association Rul
es,
Collaborative Filtering
, Genetic and Evolutionary Algorithms, and Support Vector
Machines and other Kernel
-
Based Learning Methods.

The relative merits and short
-
comings of the v
arious methods will also be made
.


Prerequisites:
FRE 6083

or permission o
f program
/course

director
.


Grading:

5%

Classroom participation

5
0%

Homework

4
5%

Final Project
and/
or Examination


Text

Mehmed Kantardzic
, “Data Mining
: Concepts,

Models
, Methods, and Algorithms
”,
W
iley
-
IEEE, 2002, ISBN 0471228524
.


Topics:

.

Week

Topic

1

Data Mining Concepts and Applications

2

Data Preprocessing and Data Reduction

3

Statistical Methods: Naïve Bayesian Classifier and Logistic Regression

4

Contingency Table and Linear
Discrimination Analysis

5

Cluster Analysis, Similarity Measures, A
gglomerative and Partitional Clustering

6

Decision Trees and Decision Rules

7

Association Rules

Lecture Notes
:


Introduction and Motivations:
introDataMining
1
.pdf



Preprocessing the Data:
preparingData.pdf