FRE 7851 Topics in Financial & Risk Engineering: Data Mining
Lecture periods: 2.5 hours (for 7 weeks)
Laboratory periods: 0 hours
Recitation periods: 0 hours
Credits: 1.5
Data 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 meaningful correlations, patterns and trends can 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 exchange rate, bank bankruptcies, understanding and managing financial risk, trading futures, credit rating, loan management, bank customer profiling, and money laundering analyses.
Data mining techniques covered in this course may include, for example, k-Nearest Neighbor algorithms, Classification and Regression Trees, Discrimination Analysis, Logistic Regression, Artificial Neural Networks, Multiple Linear Regression, k-Means Clustering, Hierarchical Clustering, Principal Components Analysis, Association Rules, Collaborative Filtering, Genetic and Evolutionary Algorithms, and Support Vector Machines and other Kernel-Based Learning Methods. The relative merits and short-comings of the various methods will also be made.
Prerequisites: FRE 6083 or permission of program/course director.
Grading:
5% Classroom participation
50% Homework
45% Final Project and/or Examination
Text
Mehmed Kantardzic, “Data Mining: Concepts, Models, Methods, and Algorithms”, Wiley-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, Agglomerative and Partitional Clustering |
6 |
Decision Trees and Decision Rules |
7 |
Association Rules |
Lecture Notes:
Introduction and Motivations: introDataMining1.pdf
Preprocessing the Data: preparingData.pdf
k-Nearest Neighbor Clustering: k-NearestNeighbor.pdf
Decision Trees and Decision Rules: decisionTrees.pdf
Naïve Bayesian Classifier: naiveBayesianClassifier.pdf
Regression: regression.pdf
Association Rules: AssociationRules3.pdf
Error Estimation and Reduction: AccuracyErrors.pdf
Assignments: