CSE 8331 Spring 2008
ADVANCED TOPICS IN DATA MINING

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. Dunham 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 one or more data mining tools (XLMiner, Weka, etc.)

Course Syllabus

Book:

Data Mining Introductory and Advanced Topics by Margaret H. Dunham Prentice Hall, 2003 Book Web Page

Class Notes

• Basic Slides

Projects

• Project I
• Project II
• Project III
  • Project III Data
  • Ferenc Bodon Implementation of Apriori
  • Christian Borgelt Implementation of Apriori
  • Su Yibin Implementation of Apriori
• Final Project
  • EMM:
    • EMM Code
    • EMM Installation and Execution Documentation
  • Biodegradation:
    • BDMINE - Biodegradation Database (ID & Password supplied via email)
    • BDMINE Documentation
    • For the project, please use the Aerobic Half Life Complete Set and predict either BOD or half life.

  Literature

  • Temporal Mining:
    • EMM form ICDM'04
    • "Data Mining and Machine Learning in Time Series Databases," ICDM '04 tutorial, by Dr. Eamonn Keogh
    • "Time Series Similarity Measures" KDD '00 Tutorial, by Dimitrios Gunopulos and Gautam Das
    • SAX
    • "A Symbolic Representation of Time Series with Implications for Streaming algorithms", DMKD03, by Jessica Lin and Eamonn Keogh
  • Stream Mining:
    • "Issues in Data Stream Management" by Lukasz Golab and M. Tamer Ozsu
    • Data Stream Mining Bibliography
    • "Data Stream Management and Mining" by Georges hebrail
    • "Statistical mining in Data Streams" by Ankur Jain
    • "Mining Developing Trends of Dynamic Spatiotemporal Data Streams" by Yu Meng and Margaret H. Dunham
    • "STREAM: The Stanford Data Stream Management System" by Arvind Arasu, Brian Babcock, Shivnath Babu, John Cieslewicz, Mayur Datar, and Jennifer Widom
    • "Aurora: A New Model and Architecture for Data Stream Management" by Daniel J. Abadi, Don Carney, Ugur Cetinternel, Mitch Cherniack, Christian Convey, Sangdon Lee, Michael Stonebraker, Nesime Tatbul, and Stan Zdonik
    • Eamonn Keogh's Datasets
    • NOAA National Weather Service River Forecast Centers
    • "Extensible Markov Model" by Margaret H. Dunham
    • "Extensible Markov Model" by Margaret H. Dunham - presentation
    • "Mining Developing Trends of Dynamic Spatiotemporal Data Streams" by Yu Meng and Margaret H. Dunham
    • "Active Mining of Data Streams" by Wei Fan, Yi-an Huang, haixun Wang, and Philip S. Yu
    • "The Problem of Concept Drift: Definitions and Related Work" by Alexev Tsymbalo
  • Spatial Mining:
    • Introductory Spatial Clustering Slides
    • Original BIRCH paper
    • Original DBSCAN paper
    • Original CLARANS paper
    • "STING: A Statistical information Grid Approach to Spatial Data Mining" by Wei Wang, Jiong Yang, and Richard Muntz
    • Introduction to Data Mining by Pang-Ning Tan, Michael Steinback, and Vipin Kumar (Ch 8)
    • Original COD-CLARANS paper
    • Voronoi diagram from Wolfram MathWorld
    • Location-Dependent Database Access by Faiza Najjar, Sean Kelley, and Margaret H. Dunham
    • Voronoi Diagram by Allen Miu from MIT
    • Voronoi Diagram Demo
    • Neighborhood Based Detection of Anomalies in High Dimensional Spatio-Temporal Sensor Datasets," by Nabil R. Adam, Vandana Pursnani Janeja, and Vijayalakshmi Atluri
    • Continuous Clustering of Moving Objects" by Christian S. Jensen, Dan Lin, and Beng Chin Ooi
  • Web Mining:
    • Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd, "The PageRank Citation Ranking: Bringing Order to the Web," 1998.
    • "Efficient Mining of Traversal Patterns" by Yongqiao Xiao and Margaret H. Dunham
    • "FS-Miner: Efficient and Incremental mining of Frequent Sequence Patterns in Web Logs' by Maged El-Sayed, Carolina Ruiz, and elke A. Rundensteiner
    • "Scalability of OAT" by Jason Mizher, Margaret H. Dunham, Lin Lu, and Yongqiao Xiao
    • "Discovery of Significant Usage Patterns from Clusters of Clickstream Data" by Lin Lu, Margaret H. Dunham, and Yu Meng
  • Text Mining:
    • Information Visualization at PNNL
    • "Tapping the Power of Text Mining" by Weiguo Fan, Linda Wallace, Stephanie Rich, and Zhongiu Zhang
    • What is Text Mining? by Marti Hearst
    • PubMiner:Machine Learning-based Text Mining for Biomedical Information Analysis by Jae-Hong Eom and Byoung-Tak Zhang
    • Supporting Discovery in Medicine by Association Rule Mining of Bibliographic Databases
    • The Text Mining Handbook, by Ronen Feldman and James Sanger
    • Text Mining Tutorial, by Marko Grobelnik and Dunja Mladenic, IJCAI-2003 Workshop on Text Mining and Link-Analysis

  Resources:

  • Electronic Textbook
  • Mathworld
  • Statistics Primer (Revised)
  • KDNuggets
  • ACM SIGKDD
  • The Data Mine
  • SMU Library User Education, Citing Sources: APA Style
  • IEEE Citation Style