CSCI 5833 -- Data Mining Tools and Techniques

STAT 5931 -- Research Topics in Statistics
Updated January 16, 2024

Office and Addresses

Delta 171 Phone 281.283.3805
email: boetticher@uhcl.edu
Secretary: Ms. Caroline Johnson, Delta 161 281.283.3860

Class Hours (Face-to-Face or Online)

Thursday 10:00 - 12:50, Room: Delta 237, or via Zoom (If necessary)
Zoom information may be found in the Google folder for this course.

Office Hours

Wed 12 - 4 PM, Thurs 9 - 10 AM, or by appointment. Students with appointments have priority. If the suite door is locked, then call my extension (x3805) using the phone in the hallway. Students who have an appointment will have priority over those students who don't. A Zoom session is also possible.

Teaching Assistant

Mr. Naga Sai Venkatesh Perumalla
Email: perumallan1637@uhcl.edu

TA Hours: Monday 7 - 10 PM; Tuesday 10AM to 3 PM; 7 - 10 PM; Wednesday 7 - 10 PM

If you are not willing to learn, no one can help you.

If you are determined to learn, no one can stop you. -- Zig Ziglar

Course Description

Data Mining has emerged as one of the most exciting and dynamic fields in computer science. The driving force for data mining is the presence of petabyte-scale online archives that potentially contain valuable bits of information hidden in them. Commercial enterprises have been quick to recognize the value of this concept; consequently, within the span of a few years, the software market itself for data mining is expected to be in excess of $10 billion by the end of this year.

The theoretical underpinnings of the data mining have existed for awhile (e.g., pattern recognition, statistics, data analysis and machine learning), the practice and use of these techniques have been largely ad-hoc. With the availability of large databases to store, manage and assimilate data, the new thrust of data mining lies at the intersection of database systems, artificial intelligence and algorithms that efficiently analyze data. Data mining seeks to detect `interesting' and significant nuggets of relationships/knowledge buried within data. It seeks to discover association rules, episode rules, sequential rules, etc., and it is concerned with efficient data structures and algorithms for data examination which possess good scaling properties.

There have been several success stories in this relatively young area: the SKICAT system for automatic cataloguing of sky surveys (JPL), the Advanced Scout system for mining NBA data (IBM), the QuakeFinder system for geoscientific data mining (UCLA/JPL) and the PYTHIA system for mining information from performance evaluation of scientific software (Purdue). Case studies from various domains (financial, bioinformatics, etc.) will be presented.

The traditional graduate student load is 3 courses. Be prepared to commit 15 to 20 hours per week to this course!

Course Goals

By the end of the course, you will

• Understand the data mining process.

• Have a working knowledge of different data mining tools and techniques.  

• Have an understanding of various Machine Learners (ML).

• Have a working knowledge of some of the more significant current research in the area of data mining and ML.

• Be aware of various data mining data repositories for the study of data mining.

• Be able to effectively apply a number of data mining algorithms (e.g., neural networks, genetic algorithms) to solve data mining problems from various problem domains including Financial and Bioinformatics.

• Be familiar with several successful applications of data mining.

Prerequisites

A course in artificial intelligence, machine learning, pattern recognition, algorithms, or statistics would be helpful, but is not required. Programming experience (or at least one course) in either C, C++, C#, Delphi, Java, PASCAL, or VB (using Visual Studio). If you do not meet the prerequisites, then you need to drop this course!  

Methodology

Lecture, seminar, case studies, and interactive problem solving.

Appraisal:

Grades will be based solely on criteria listed above. No other factors will be considered.

Grading Scale

    93+ = A; 90 = A-; 87+ = B+; 83+ = B; 80+ = B-;

      77+ = C+; 73+ = C; 70 = C-; 67+ = D+; 63+ = D; 60+ = D-; 0+= F

My motto:

Foster disciplined, altruistic passion.

Required Textbook

     None.

Reference Books

1. Aggarwal, Charu C. Data mining: the textbook. Springer, 2015.

2. Berry and Linoff, Data Mining Techniques, Wiley, 2000.

3. Han, Jiawei, Jian Pei, and Micheline Kamber. Data mining: concepts and techniques. Elsevier, 2011.

4. Mitchell, Machine Learning, McGraw-Hill, Boston, 1997.

5. Witten, Ian H., and Eibe Frank. "Data mining: practical machine learning tools and techniques with Java implementations." ACM Sigmod Record 31.1 (2002): 76-77.

Other Reference Materials

• KDnuggets: Data Mining, Web Mining, and Knowledge Discovery Guide: A portal for data mining information.
• The Data Mine: An index of resources for data mining.
• Data Mining Competitions, KDD Cup, http://www.data-mining-cup.com, Kaggle.com

Conferences, Journals, and Organizations

• ACM's Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD): A premier group promoting knowledge discovery R&D. Organizes one of the top conferences in the area.
• IEEE ICDM: IEEE International Conference on Data Mining.
• ACM's Special Interest Group on Management of Data (SIGMOD): Another ACM SIG that contributes to data mining research.
• SIAM's data mining conference (SDM) 2019 
• PAKDD 2019: Pacific Asia conference on knowledge discovery in data.
• IEEE Transactions on Knowledge and Data Engineering: a premier journal.
• Data Mining and Knowledge Discovery
• Knowledge and Information Systems - An International Journal
• IEEE Transactions on Pattern Analysis and Machine Intelligence
• Data Mining Group: a data mining standardization organization (e.g. PMML - predictive model markup language).

Data Resources

• Dataset Search Google

• UCI Machine Learning Database Repository

• 2010 Census Data

• Inter-University Consortium for Political and Social Research (ICPSR) The ICPSR database contains a wealth of statistical studies covering a wide variety of topics. The ICPSR has a number of search aids available: Quarterly Cumulative List of New Releases

• Health and Medical Care Archive

• National Archive of Computerized Data on Aging

• National Archive of Criminal Justice Data

• Substance Abuse and Mental Health Data Archive

• United Nations Data

• Clinical Trials

• U.S. Government Data

• https://dreamtolearn.com/ryan/1001_datasets

• http://www.kdnuggets.com/datasets/index.html

• http://www.autonlab.org/autonweb/downloads/datasets.html

• http://mulan.sourceforge.net/datasets-mlc.html

• http://www.datasciencecentral.com/profiles/blogs/20-free-big-data-sources-everyone-should-check-out

• http://www.fki.inf.unibe.ch/databases

• http://openscience.us/repo/

• https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets

• https://www.reddit.com/r/datasets/

• http://www.uco.es/~in1rosaj/utiles/datasets.html

• http://www.cc.uah.es/drg/c/RHH_RAISE12_Repos.html

• https://en.openei.org/datasets/dataset/geothermal-data-repository-gdr

• https://datahub.io/dataset

• http://fmwww.bc.edu/ec-p/data/wooldridge/datasets.list.html

• http://www.un-spider.org/news-and-events/news/geospatial-data-datasets-repository-list-available

• http://wadam-data.dis.uniroma1.it/

• https://www.arts.gov/grants-organizations/research-art-works/publicly-available-data-sources

• https://en.wikipedia.org/wiki/List_of_neuroscience_databases

• http://appliedpredictivemodeling.com/data/

• https://dataverse.harvard.edu/

• https://www.quandl.com/

• http://www.cs.ucr.edu/~eamonn/time_series_data/

• http://robjhyndman.com/TSDL/

Bioinformatic and biological databases:  

Feb 22 –  Decision Trees

Assignment 2 is due.

Feb 29 – Decision trees continued, Midterm Review

FOR NEXT WEEK (IF NOT SOONER)  

·  Submit:  Midterm questions by Wednesday, March 6^th, 7 PM.

                Use the template found on the Google Drive

                Strip out any identifying information (Your name, Student ID number)

                Specify whether you want me to post your questions on the Google Drive.

·   Study!

Mar 07 – Midterm

Assignment 3 is due.

FOR NEXT WEEK (IF NOT SOONER)

·   Read documents in: WK08 Notes - Clustering, Instance Based Learning, SVM, Ensemble Learning.zip

·   Review:  The following K-Means applet:

      http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/AppletKM.html

 ·   Read:  No assigned readings for next week

 ·   Review:  The following SVM applet:

      http://svm.dcs.rhbnc.ac.uk/pagesnew/GPat.shtml

Mar 14 ************ Spring Break *************

Mar 21 – Ensemble Learning, Random Forests, Clustering, Instance-Based Learning, SVM. Part 1

Mar 28 – Ensemble Learning, Random Forests, Clustering, Instance-Based Learning, SVM. Part 2

FOR NEXT WEEK (IF NOT SOONER)

·  Review:  The following tutorial on Genetic Algorithms:

            http://www.obitko.com/tutorials/genetic-algorithms/

·   Read documents in: WK10 Notes - Genetic Algorithms.zip

Apr 04 – Genetic Algorithms, Genetic Programs

Assign Homework 4

Point value: 100 points

Due date:  Thursday, April 18^th, 10:00 AM

FOR NEXT WEEK (IF NOT SOONER 

·   Review:  One (or more) of the following online neural network tutorials:

NN Tutorial 1

NN Tutorial 2

NN Tutorial 3

·   Read documents in: WK11 Notes and Papers - Neural Networks.zip

·   Run:    WK11 R Example - Self Organizing Map.R

·   Review:  Download, install, and try GDB_Net (A neural network software package).

·   Review:  Try out this Kohonan Self-Organizing Map applet

                 Click here for the zipped code of this applet.

******** April 9 – Last day to withdraw ********

Apr 11 – Neural Networks including Perceptron, Backprop., Self-Organizing Map (SOM)

Assign Homework 5

Point value: 100 points

Due date:  Sunday, April 28^th, 4:00 PM

FOR NEXT WEEK (IF NOT SOONER)  

·   Read:  WK12 Notes - Neural Networks - CNNs and GANs

·   Read:  WK12 NotesB - The Rectified Linear Unit (ReLU) Activation Function

·   Read:  WK12 NotesC - A Gentle Introduction to the Progressive Growing GAN

·   Read:  WK12 NotesD - A Gentle Introduction to StyleGAN the Style Generative Adversarial Network 

Apr 18 – Neural Networks - Convolutional Neural Networks,  Generative Adversarial Networks (GANs)

Assignment 4 is due.

Apr 25 – Evaluating Data Mining results, Final Exam Review

May 02 - Final Exam

Other Policies

This class has 6 simple rules:

 1) Be respectful of others. (3% Penalty/Cellphone or text infraction)

 2) Be very passionate about your learning and do your best.

 3) Be fearless - ask lots of questions in class.

 4) Don't be late on anything. (10% Penalty/Day)

 5) Don't ever cheat.

 6) Have fun!

Miscellaneous

• Any person with a disability who requires a special accommodation should inform me and contact the Disability services office or call 281 283 2627 as soon as possible.

• You are expected to come fully prepared to every class!

• If there is any religious observance that may interfere with any scheduled exam, homework due date, or attending class, please notify me of the situation during the first 2 weeks of class so that adjustments can be made at that time.

• Please turn off all cell phones, and pagers prior to the start of class.

• I am willing to provide letters of recommendation/references only if you have attained an 'A' in one of my classes, or two 'A-' in two of my classes.

• I highly recommend that you seek out your advisor and complete your Candidate Plan of Study (CPS) as soon as possible. I am normally not available for advising during the summer months.

Return to Top

Home • UHCL • SCE

2700 Bay Area Boulevard
Delta Building. Office 171
Houston, Texas 77058
Voice: 281-283-3805
Fax: 281-283-3869
boetticher@uhcl.edu

© 2002-2024 Boetticher: Data Mining Course, All Rights Reserved.