CSE/EMIS 8331: Data Mining, Spring 2012

> Course Syllabus

Course Description

This is a second course in Data Mining. A prerequisite is successful completion of CSE 7331 or other Introductory Data Mining course. Please contact Dr. Hahsler if you have concerns or questions about this prerequisite. It is assumed that every student is familiar with 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, Weka, XLMiner, etc.). The objective of this course is to get an overview of several advanced data mining techniques and understand the research methods applied in the field.

Assignments

• Reviewing Papers: We will discuss 1-2 research papers almost every Wednesday (see syllabus) in class. Here are the guidelines and the review form.
• Tutorial: Every student will prepare and present a tutorial. Tutorials are held typically on Mondays. A list with exact times and topics will be published on this Web site. Here are the guidelines for preparing the tutorial. Note: All students need to participate with questions and a discussion on the presented topics.
• Final Project: For the final project you have a choice between a data mining project, implementing a data mining method or writing a review paper. The topic of the final project should be coordinated with the tutorial's topic. Here are the guidelines for the final project and the presentation schedule.

Project Presentation

• Recorded presentation by Ryan Zauber.

Tutorial Topics

Papers for Review

Reference Text (Recommended but not required)

• Data Mining Introductory and Advanced Topics by Margaret H. Dunham, Prentice Hall, 2003. Book Web Page
• Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Addison Wesley, 2005. Book Web Page
• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman, 2nd edition, Springer, 2009. Book Web Page
• Mining of Massive Datasets by Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2011. Book Web Page

Links

• ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
• ACM SIGMOD Conference (SIGMOD)
• European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
• Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)
• SIAM Conference on Data Mining (SDM)
• IEEE International Conference on Data Mining (ICDM)

• ACM Transactions on Knowledge Discovery from Data (TKDD) - ACM
• Data Mining and Knowledge Discovery (DAMI) - Springer
• Data & Knowledge Engineering (DKE) - Elsevir
• Knowledge and Information Systems: An International Journal - Springer
• IEEE Transactions on Knowledge and Data Engineering (TKDE) - IEEE
• Intelligent Data Analysis: An International Journal - IOS Press
• Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery - Wiley

• ACM Digital Library
• CS Digital Library (IEEE Comp. Society)
• Google Scholar
• CiteSeerx
• arXiv.org Computer Science

• KDnuggets: Competitions
• Kaggle Competitions
• KDnuggets: Dataseta
• UCI KDD Archive

High performance computing

• Apache Hadoop: MapReduce, Pig, Hive (Cloudera)
  • MapReduce: Simplified Data Processing on Large Clusters
  • PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce
  • Filtering: A Method for Solving Graph Problems in MapReduce
  • Map-Reduce for Machine Learning on Multicore
• Storm (reliabe processing of unbounded streams of data in realtime)
• Spark (fast and general engine for large-scale data processing)
• HPCC Systems (by LexisNexis)
• Deep Learning with H2O and R

Tools for distance students

• CamStudio: Free Screen recording software for Windows.