CENG 452 Data Mining (A. Kurt).docDocument Transcript
CENG 452 Data Mining (A. Kurt)
2004 Fall Semester
Term Project (Due Dec 31)
In this course, you are supposed to complete and submit a project as explained below.
• Choose a subject from list below (you may choose an alternative subject by Thursday 28 Oct and let me
know in the class then.) Note that everyone is assigned a default subject already.
• Read one or a couple book chapters (for most projects I will give you book, chapters etc). Try to
understand the subject. You may need to read the material a few times to understand it thoroughly.
• Prepare a powerpoint presentation along with a short essay about the subject to summarize/highlight.
• Make a presentation of the subject in class.
• Find a data set in the subject area (use references in the material, check out the data mining sites, I point
out some important links below. Avoid using well known data sets such as those used in courses,
teaching, etc.) Please check with me before you go any further with the data. It is desired that you work
with real data instead of synthetic data.
• Perform a data mining session to find out hidden, interesting, usefull knowledge, patterns, trends in data.
You may need to prepare data for data mining. You are basically free to choose any tool you want. You
need to create a report about the data mining session, detailing out all step in data mining from goal
identification to taking action. (7 Step KDD process, chapter 5 in the book) Remember you may have to
repeat data mining session several times before you can have satisfactory or interesting result each time
modifiying some part of the experiment which could be changing training set instance, or input
attributes, or, data mining parameters etc. The more the result, the better the project. You will try your
best in hunting down the best outcome from the data.
Inputs: A subject and material.
• A short article about the subject (5-10 pages, grading based on presentation of subject and language use,
copy-paste or slight modifications will be considered as cheating) Submit soft and hard copy.
• A presentation of the material (10-30 slides, haft an hour, grading based on slide contents, language and
presentation/communication ability) Submit soft copy
• Finding data set (grading based on the quality of data, you may list alternative data sets explain why you
choose this one) Submit softcopy
• Performing data mining on data set (grading based on outcome of data mining, amount of data mining,
usually more sessions may reveal more hidden knowledge, please keep track of time you spend and
number of sessions you did)
• Data Mining Report (Report should discuss all relevant aspects of KDD process, grading based on
content, presentation, language) Submit soft and hard copy.
Grading: To be announced later
Ayse ÖzTürk -Bioinformatics Chapter 10 in Data Mining: Multimedia, Softcomputing, Bioinformatis
Hüseyin Hakkoymaz -Weather Forecast Project
Investigative Data Mining for Security and Criminal Detection
Chapter 1 - Precrime Data Mining
Chapter 2 - Investigative Data Warehousing
Chapter 3 - Link Analysis: Visualizing Associations (M Bilgi)
Chapter 4 - Intelligent Agents: Software Detectives
Chapter 5 - Text Mining: Clustering Concepts (M Bilgi)
Chapter 6 - Neural Networks: Classifying Patterns (E Postacı)
Chapter 7 - Machine Learning: Developing Profiles (E Postacı)
Chapter 8 - NetFraud: A Case Study
Chapter 9 - Criminal Patterns: Detection Techniques (F Değirmenci)
Chapter 10 - Intrusion Detection: Techniques and Systems (F Değirmenci)
Chapter 11 - The Entity Validation System (EVS): A Conceptual Architecture
Chapter 12 - Mapping Crime: Clustering Case Work
Web Data Mining and Applications in Business Intelligence and Counter-Terrorism
by Bhavani Thuraisingham ISBN:0849314607
Chapter 9 - Data Mining and the Web (Suat Mercan)
- Processes and Techniques for Web Data Mining (Suat Mercan)
- Mining Databases on the Web (Yemen Açıkgöz)
- Information Retrieval and Web Data Mining (Yemen Açıkgöz)
- Information Management and Web Data Mining (A Aslan)
- Semantic Web Mining (A Aslan)
- Mining Usage Patterns and Structure on the Web (M E Bodur)
- Prototypes, Products, and Standards for Web Data Mining (M E Bodur)
- Some Applications for Web Mining (M A Şengül)
Chapter Some Information on Terrorism, Security Threats, and Protection Measures
18 (M A Şengül)
- Web Data Mining for Counter-Terrorism (A Ertekin)
- Mining Web Databases for Counter-Terrorism (A Ertekin)
- Information Retrieval and Web Mining for Counter-Terrorism (A Dagci)
Chapter - Information Management and Web Mining for Counter-Terrorism (A
- Semantic Web Mining for Counter-Terrorism
- Web Usage and Structure Mining for Counter-Terrorism
- National Security, Privacy, Civil Liberties, and Web Mining
- Revisiting Security Threats with Respect to Web Mining
- E-Commerce, Business Intelligence, and Counter-Terrorism
- Summary and Directions