last updated: October 30, 2014

COSC 6335: Data Mining in Fall 2014 (Dr. Eick )



Goals of the Data Mining Course

Data mining centers on finding valid, novel, interesting, and potentially useful patterns in data. It aims at transforming a large amount of data into a well of knowledge. Data mining has become a very important field in industry as well as academia. For example, almost 900 papers were submitted for the IEEE International Conference on Data Mining (ICDM) to be held in Shenzhen, China in December 2014 (Data Mining Conference Rankings). Data mining tools and suites (for example, see KDnuggets' DM Software Survey) are used a lot in industry and in reseach projects. UH's Data Mining and Machine Learning Group Website (UH-DMML) conducts research in some of the areas that are covered by this course (UH-DMML Research Overview). Finally, having basic knowledge in data mining is a plus when you are looking for a job in industry and at major US research institutions, such as the Texas Medical Center in Houston or at Federal Research Labs.

The course covers the most important data mining techniques and provides background knowledge on how to conduct a data mining project. In the first 9 weeks a very basic introduction to data mining will be given. After defining what knowledge discovery and data mining is, data mining tasks such classfication, clustering, and association analysis will be discussed in detail. Exploratory data analysis, centering on basic visualization techniques and statistics, to get a better understanding of the data mining task at hand will be covered. Moreover, techniques how to preprocess a data set for a data mining task will be introduced. In the remaining 4-5 weeks of the semester, more advanced topics including spatial data mining, advanced clustering and classification techniques, and sequence mining and, webpage ranking will be discussed. Moreover, in course projects you will obtain hands on experience in conducting data mining project. Finally, as R will be used in most course projects; therefore, participants of the couse will obtain valuable exprience in using the R statistics, data mining, and visualization packages and will learn how to write programs in R and how to develop data mining software on top to R. A recent 2013 poll Rexer Analytics found that R is currently the most popular data mining tool: 24% of the respondents use R as their primary tool, and only 30% of the respondents do not use R at all. Although R is a domain specific language, it's versatile.

In summary, having a sound background in data analytics and data mining and knowing R well will open a lot of job opportunities for you, which, I believe, is a strong reason to take the course; perhaps, you have to work a little more when taking this course, compared to other courses, and perhaps not everybody taking this course will get an A, but you should also consider the merits of completing this course!

Comments concerning this website

If you have any comments concerning this website, send e-mail to: ceick@uh.edu

Basic Course Information

Instructor: Dr. Christoph F. Eick
office hours (573 PGH) TU 10-11a TH 2-3p
e-mail: ceick@uh.edu
TA: Arko Barman
office hours: TU 2-3 TH 3-4p in ????
2014 TA website: Arko Barman's COSC 6335 Website

Google COSC 6335 News Group
class meets: TU/TH 11:30a-1p
cancelled classes: TBDL
Makeup class (if necessary): Tu., December 9, 11:30a-1p
COSC 6335 Lecture Video Link

Course Materials

COSC 6335 Syllabus
Objectives Data Mining Course

Required Text:
P.-N. Tang, M. Steinback, and V. Kumar: Introduction to Data Mining,
Addison Wesley, 2006,
Link to Book HomePage

Recommended Texts:
Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques
Morgan Kaufman Publishers, Third Edition, 2011.
Link to Data Mining Book Home Page

NIST/SEMATECH e-Handbook of Statistical Methods (good onlne source covering exploratory data analysis, statistics, modelling and prediction)

News COSC 6335 (Data Mining) Fall 2014

Important Dates in 2014

Th., September 4: Arko Barman (the TA of this course) will be teaching an R-Lab centering on R basics and knowledge necessary for Project1 (please bring your labtop with R installed and read the Project1 specification prior to the lab; it will be posted no later than September 2 on this website)
Th., November 6: Midterm Exam
Tu., November 4: Guest lectures will be teachina those day(s), as Dr. Eick will be attending the ACM SIGSPATIAL GIS Conference in Dallas in that week.
Th., November 20: Project3 Student Presentations in 563 PGH
Tu., December 9, 11:30a-1p: Potential Makeup Class for cancelled classes...
Th., December 11, 11a-2p: Final Exam
Review Sessions(last 35 minutes of lecture): October 2, October 21, either December 4 or December 9

2013 Reviews

Review1 (try to solve problems 2-4! Solutions to be discussed on October 8)

Review2 (solution sketches; the answer to question 2d has been corrected (in red); was discussed during the lecture on October 31).

Review3 (will take place on Tuesday, December 10. Solve the 7 review questions!)

Prerequisites

The course is mostly self-contained. However, students taking the course should have sound software development skills and very basic knowledge of Java.

Course Elements and Their Tentative Weights for 2014

Course Projects (4): 41%
Exams (2): 58% (midterm: 26%; final exam: 32%)
Class Attendance: 1%

2014 Projects

Project1: Exploratory Data Analysis for a Banknote Authentication Dataset using R (Group Project; groups of size 3 (or 2); deadline Fr. September 26, 11p (electronic submission)).

Project1 Groups

Teaching Material for Project1: Some useful R code, Decision Trees in R.

Project2: Traditional Clustering with K-means and DBSCAN; very pleliminary draft; you are expected to start working on the project after the lecture on Th., October 2 (Individual Project, Randomized Hill Climbing Slides, Hints Task4 Project2, Post Analysis Project2 2013)

Project 3: Something Interesting About Finding Interesting Associations in Data (Group Project; group sizes 3-4)

Project4: Reading, Understanding, Summarizing and Reviewing of Data Mining Papers (2-person Group Project; paper candidates (choose one!): Paper A (Best KDD 2014 Student Paper), Paper C (Best ICDM 2013 Paper), Paper E (Best SDM 2014 Student Paper).

Student Group Assignments Projects 1, 3 and 4

COSC 6335: Data Mining Lecture Notes

I Introduction to Data Mining (Part1, Part2, Part3, Differences between Clustering and Classification --- covers chapter 1 and Section 2.1)
II Exploratory Data Analysis (covers chapter 3 in part; see also Interpreting Displays)
III Introduction to Classification: Basic Concepts and Decision Trees
IV Introduction to Similarity Assessment and Clustering (AGNES (not covered) and DBSCAN; R-scripts demonstrating: K-means/medoids, DBSCAN)
V Association Analysis(Part1,Part2)
VI A Short Introduction to Data Cubes
VII Preprocessing for Data Mining
VIII Introduction to Spatial Data Mining (Introduction to Regional Knowledge Extraction, Intoduction to Region Discovery, Example Fitness Functions (to be used in the Region Discovery Lecture), Introduction to the CLEVER Region Discovery Algorithm, Dr. Eick's Report 2011 ACM-GIS Conference, Spatial Regression(not covered in 2011))
IX More on Clustering and Outlier Detection: Grid-based, Density-based Clustering, and Subspace Clustering ( Non-Parametric Density Estimation R Demo), Cluster Validity, Anomaly/Outlier Detection.
X Introduction to R (R Preliminaries, R Data Structures, R Graphics, Functions in R), Rar-file (contains the .r (dot r)files are the scripts presented in class, namely kmean.r, linearRegression.r, linearRegression_iris_2.r, and scatterlot3d_1.r. The file RCommander_LAB_EXAMPLES.r contains all the scripts. The rest are the powerpoints and the datasets used ), Sample Classification Plots in R)
XI More on Classification: Instance-based Learning, Support Vector Machines, Editing, Ensembles, and ROC-Curves (NN-Classifiers and Support Vector Machines (Prof. Sastry's Introduction to SVM), Editing and Condensing Techniques for NN-Classifiers (to be covered in Fall 2013), Ensembles and ROC Curves, Model Evaluation).
XII The PageRank Algorithm (taught the first time in 2012; Gleich 2009 Dissertation Defensecentering on PageRank at Stanford University)
XIII Top Ten Algorithms in Data Mining (Top-10 Panel, Top10)
XIV Miscellaneous: Experiences in Finding Data Mining/Internet/HPC Jobs in Industry, 2009 Netflix Contest, 90 Days at Yahoo! and Final Words

Order of Teaching in 2014 (subject to change): I-II-IV-III--V(Part1)-V(Part2)-VII-XI-VIII-IX(only covering hierarchical clustering, DENCLUE and subspace clustering in 2013)-XII-XIII-VI-XIV.
Remarks: likely, topios XIII and XIV, will be only partially covered. R (Topic X) will be covered in a lab early September and as part of the lectures centering on Exploratory Data Analysis and Clustering Part1.

2013 Projects

Project1: Exploratory Data Analysis for a Pima Indians Diabetes Dataset using R( Post Analysis Project1; Group Project; groups of size 3 (or 2)).

Project2: Traditional Clustering with K-means and DBSCAN (Individual Project, Yeast CSV-File, Randomized Hill Climbing Slides, Hints Task4 Project2, Post Analysis Project2, Project2 Scores (scores marked by '?' are only preliminary and might change))

Project 3: Something Interesting about Finding Interesting Associations in Large Amounts of Data (Group Project (Groups of 4), Project3 Q&A, Project3 Scores)

Project4: Reading, Understanding, Summarizing and Reviewing of Data Mining Papers (2-person Group Project; paper candidates (choose one!): Paper 1, Paper 2, Paper 3, Paper 4; Project4 Scores)

2013 Project Weights: Project1:1, Project2:2.0, Project3: 1.3, Project4: 1.2.

2013 Project Scores (please, verify!)

2011 Review Sessions

The review sessions will discuss questions which typically will be posted 1-5 days prior to the review session; review sections will take about 30 minutes and are typically discussed 10:45-11:15a. It is important that you try to answer the review questions before the review session!

2012 Review Questions
Questions 2012 Review1
Question 2012 Review2
Question 2012 Review3

2011 Review Questions
Questions October 4
Questions October 20
Questions November 22
Questions December 1

A Few Results 2012 DM Questionnaire

Student Preferences and other: 19 students joined UH in Fall 2012; 15 students joined UH earlier.

Student Languages: As far as languages are concerned which students spoke as a child are concerned (based on 34 responses; if students listed more than 2 languages only the first two languages were counted): English(22), Telugu(12), Hindi(12), Chinese(4), Tamil(2), Greek(2), Bulgarian(1), Arab(1), Urdu(1), French(1), Persian(1), Marathi(1), Malayalam(1).

A Few Results 2011 DM Questionnaire

Student Preferences: Of a group of 31 students (neutral statements were not counted), 26 students like group projects and 3 students dislike group projects; 25 students like reading scientific papers and 4 students dislike reading scientific papers; 23 students like projects which involve a significant amout of programmming and 6 students dislike such projects; 17 students like to give presentations and 5 students dislike giving presentations.

Student Languages: As far as languages are concerned which students spoke as a child are concerned (based on 29 responses; if students listed more than 2 languages only the first two languages were counted): English(16), Telugu(6), Hindi(6), Chinese(5), Tamil(5), Spanish(3), African(1), French(1), Nepali(1), Marathi(1), Kannada(1).

2011 Projects

Project1: Explaratory Data Analysis for the Vehicle Silhouette Dataset using R.
Project2: Clustering with K-means and DBSCAN
Project 3: Extracting Regional Knowledge from Spatial Datasets: Clustering with Plug-in Interestingness Functions with CLEVER
Project 4: Something Interesting About Finding Something Interesting (Group Project; Slides Project4 Student Presentations, Video taken of the Event)
Project 5: Learning and Assessing Classification Models

2010 Assignments

Assignment1 (see Chun-sheng's Website)
Assignment 2
Assignment3 (Earthquake 2010 Dataset)
Assignment4
First Draft of Assigment 5

2010 Review Sessions

There will be 30 minute review sessions on September 23, October 12, November 16, and November 30. Review questions will be posted here. Occasionally, review questions will discuss paper-and-pencil problems of assignments.

Review Questions for September 23
Review Questions for October 12
Review Questions for November 11
Review Questions for November 30

Grading

Each student has to have a weighted average of 74.0 or higher in the exams of the course in order to receive a grade of "B-" or better for the course. Students will be responsible for material covered in the lectures and assigned in the readings. All assignment and project reports are due at the date specified. No late submissions will be accepted after the due date. This policy will be strictly enforced.
Seveal times during the semester I will check class attendance at randomly chosen dates, and an attendence score will be computed from how many of the those lectures you attended.

Translation number to letter grades:
A:100-90 A-:90-86 B+:86-82 B:82-77 B-:77-74 C+:74-70
C: 70-66 C-:66-62 D+:62-58 D:58-54 D-:54-50 F: 50-0

Only machine written solutions are accepted (the only exception to this point are figures and complex formulas) in the assignments. Be aware of the fact that our only source of information is what you have turned in. If we are not capable to understand your solution, you will receive a low score. Moreover, students should not throw away returned assignments or tests.

Students may discuss course material and homeworks, but must take special care to discern the difference between collaborating in order to increase understanding of course materials and collaborating on the homework / course project itself. We encourage students to help each other understand course material to clarify the meaning of homework problems or to discuss problem-solving strategies, but it is not permissible for one student to help or be helped by another student in working through assignment problems and in the course project. If, in discussing course materials and problems, students believe that their like-mindedness from such discussions could be construed as collaboration on their assignments, students must cite each other, briefly explaining the extent of their collaboration. Any assistance that is not given proper citation may be considered a violation of the Honor Code, and might result in obtaining a grade of F in the course, and in further prosecution.

Past Data Mining Exams

2008 Midterm Exam
2007 Final Exam
2009 Midterm Exam with Solution Sketches
2009 Final Exam with Solution Sketches
2010 Midterm Exam with Solution Sketches
2011 Midterm Exam with Solution Sketches (some typos in the solution of Problem 5b have been corrected on November 29, 2011)
2012 Midterm Exam with Solution Sketches
2013 Midterm Exam with Solution Sketches
2010 Final Exam with Solution Sketches
2011 Final Exam with Solution Sketches

Summary Answers COSC 6335 2009 Student Questionnaire

Student Language Summary Registered Students: English:14, Hindi:9, Telugu:7, Bengali:2, Vietnamese:2, Arabic:2, Sindhi:1, French:1, Russian:1, Turkish:1, Kyrgyz(?):1, Tamil:1, Filipino:1, Spanish:1, Urdu:1, Garhwali(?):1, Chinese:1; I am impressed: some of you spoke up to four languages as a child! Concerning group projects, 11 students liked group projects, 2 students disliked group project, and 9 students had no preference. Concerning reading scientific papers 12 students liked reading scientific papers, 3 students disliked it, and the rest of the students were neutral or gave fuzzy answers "I like reading paper that are interesting.". 15 students like giving presentations and 4 students didn't. Concerning projects that involve significant amounts of programming 16 liked it and 3 didn'tlike it.

Master Thesis and Dissertation Research in Data Mining

If you plan to perform a dissertation or Master thesis project in the area of data mining, I strongly recommend to take the "Data Mining" course; moreover, I also suggest to take at least one, preferably two, of the following courses: Pattern Classification (COSC 6343), Artificial Intelligence (COSC 6368) or Machine Learning (COSC 6342). Furthermore, knowing about evolutionary computing (COSC 6367) will be helpful, particularly for designing novel data mining algorithms. Moreover, having basic knowledge in data structures, software design, and databases is important when conducting data mining projects; therefore, taking COSC 6320, COSC 6318 or COSC 6340 is a also good choice. Moreover, taking a course that teaches high preformance computing is also desirable, because most data mining algorithms are very resource intensive. Finally, having some knowledge in the following fields is a plus: numerical optimization techniques, image processing, statistics, geographical information systems (GIS), agent-based systems and data visualization.

Also be aware of the fact that having sufficient background in the above listed areas is a prerequisite for consideration for a thesis or dissertation project in the area of data mining. I will not serve as your MS thesis or dissertation advisor, if you have do not have basic knowledge in data mining, machine learning, statistics and related areas. Similarly, you will not be hired as a RA for a data mining project without having some background in data mining.

Data Mining Links

KDnuggets
2013 Rexer Analytics Data Mining Software Highlights
Netflix $1,000,000 Grand Prize
SPMF (Sequential Pattern Mining Framework)
Magnum Opus Data Mining Framework
UIUC Data Mining Group
Microsoft DMX Group
UMN Spatial Database and Spatial Data Mining Group
Data Mining and Machine Learning Group University of Helsinki
Houston R Group
UH's Data Mining and Machine Learning Group (UH-DMML)
Data Mining Conferences and Journals
RapidMiner (formerly Yale)