EMIS, Lyle School of Eng., SMU

EMIS/CSE 8331: Data Mining, Spring 2015

Dr. Michael Hahsler

> Course syllabus

Course Description

This second course in data mining focuses on understanding the research methods used in the field of data mining. The course targets students who want to gain in-dept knowledge about a particular data mining topic (e.g., PhD students who plan to use a data mining component in their research).

Prerequisites: Successful completion an introductory data mining course like EMIS 7332/CSE 7331. It is assumed that every student is familiar with all the basic data mining topics (clustering, classification, and association rules) and has some experience with programming and one or more data mining tools (R, RapidMiner, SAS, SPSS, Weka, XLMiner, etc.). Enrolling in this course requires department/instructor consent.

Project Presentation Video

Tutorial Topics

Date Presenter Tutorial Abstract Additional material (code, software, etc.)
1/27 Michael Hahsler Data Stream Mining R package stream, MOA, Apache Storm, Apache Samza, Apache SAMOA, IBM InfoSphere Streams, MS StreamInsight
2/9 Jake Drew Biological Sequence Classification abstract BiostringsTools
2/9 Pamprapai Thainiam Churn Prediction with Support Vector Machines abstract
2/16 Sri Surya Nulu Opinion Mining abstract
2/16 Chatchai Wangwiwattana Data Mining with Outliers: RANSAC abstract Robust statistics (R), RANSAC (Python)
2/23 2/25 Raymond Martin Pattern Mining in Streams abstract
2/23 2/25 Joseph Kasonde Ensemble Classification abstract R examples
2/25 (Wed) Sheila Lincoln Bayesian Networks abstract
3/2 Taghreed Alghamadi and Ali Almadan Energy Load Mining: Time series analysis abstract ARIMA Models in R (YouTube)
3/16 Chris Ayala Social Network Mining abstract Gephi, Cytoscape
3/16 Richard Ernst Name Disambiguation abstract
3/23 Josh Smitherman Latent Dirichlet Allocation (LDA) abstract Code
3/30 Hala El-Ali Big Data Technologies abstract
4/6 Tao Shan Item-based Collaborative Filtering abstract
4/6 Marie Vasek Web-based Malware Detection
4/13 Austin Rosel Artificial Neural Networks - video abstract R examples

Michael Hahsler
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