CS 8331: Data Mining (crosslisted as EMIS 8331), Spring 2019

All course material is provided under

Instructor: Dr. Michael Hahsler
Syllabus and assignments: Canvas

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-depth 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/CS 7331 or CS 7324. It is assumed that every student is familiar with all the basic data mining topics (clustering, classification, and association rules) and has experience with programming and one or more data mining tools (R, Python). Enrolling in this course requires department/instructor consent.

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 Tuesdays. A list with exact times and topics will be published on this Web site. Here are the guidelines for preparing the tutorial and the used grading form. Note: All students need to participate with questions and a discussion on the presented topics.
Final Project: For the final project you need to implement 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.

Tutorials

Date	Presenter	Tutorial	Abstract	Additional material (code, software, etc.)
1/23	Michael Hahsler	Data Stream Mining	Abstract	R package stream, MOA, Apache Spark Streaming, Apache Storm, Apache Samza, Apache SAMOA, IBM Streams, MS Azure Stream Analytics
2/6	Mohamed Elsaied	Hybrid Recommender Systems	Abstract
2/13	Mengli Hua	Large Scale Recommender Systems	Abstract	Example (R Code), R package recommenderlab
2/20	Bowei Tian	Machine Learning for Time Series Data	Abstract
2/27	Liang Ma	Regularization in Data Mining	Abstract	R examples for Regularization
3/6	Ian Johnson	Ensemble Classification Methods	Abstract	Example code
3/27	Bolong Zhang	Web Usage Mining	Abstract
4/3	Justin Ledford	Adversarial Inputs to Image Classification with Neural Networks	Abstract
4/3	Zohreh Raziei	Reinforcement Learning	Abstract	thompson_sampling.R, upper_confidence_bound.R, Ads_CTR_Optimisation.csv
4/10	Jake Carlson	Community Detection in Python	Abstract
4/10	Jiaqi Song	Vehicle routing problems with time windows	Abstract
4/17	Xingming Qu	Sentiment Analysis	Abstract
4/17	Roger Stanton	Network Analysis	Abstract	R code, network.csv, stations.csv.

Paper Reviews

Due Date	Paper	Additional Material (code, papers, etc.)
2/6	Clustering Using a Similarity Measure Based on Shared Near Neighbors, IEEE Transactions on Computers, 22(11), 1025-1034, 1973. (download from CiteSeer)	Jarvis-Patrick clustering in R, SNN clustering paper
2/13	ABACUS: Mining Arbitrary Shaped Clusters from Large Datasets based on Backbone Identification, SIAM International Conference on Data Mining, 2011	Partial ABACUS implementation, clustering images, DBSCAN paper, clustering with dbscan in R, more dbscan examples, a unified view
2/20	Dissimilarity plots: A visual exploration tool for partitional clustering, Journal of Computational and Graphical Statistics, 2011	R package seriation, Dissimilarity plot in R
2/27	Clustering of Time Series Subsequences is Meaningless, IEEE International Conference on Data Mining, 2003	Other papers: An Alternate Measure for Comparing Time Series Subsequence Clusters, Making Subsequence Time Series Clustering Meaningful. Code: Using R for time series analysis, time series example code, Cylinder-Bell-Funnel example, subsequence clustering example with real data
3/6	Temporal structure learning for clustering massive data streams in real-time, SIAM International Conference on Data Mining, 2011	rEMM package, code example from paper
3/27	Clustering data streams based on shared density between micro-clusters, IEEE Transactions on Knowledge and Data Engineering, 2016	Implementation in the R package stream
4/3	Integrating Classification and Association Rule Mining, KDD'98	LUCS-KDD Implementations, R package arulesCBA, arulesCBA example. Classification Using Association Rules: Weaknesses and Enhancements.
4/10	Interpretable regularized class association rules algorithm for classification in a categorical data space, Information Sciences 2019	R package arulesCBA, about rulefit, some R code examples.
4/17	Approximate Ranking from Pairwise Comparisons, International Conference on Artificial Intelligence and Statistics 2018	R code for H-LUBC, R package relations

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-Verlag, 2009. Book Web Page
Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2011. Book Web Page

Tutorials from Previous Semesters

High performance computing

Apache Hadoop: MapReduce, Pig, Hive (Cloudera)
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.

Michael Hahsler
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