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CENG 452 Data Mining (A. Kurt)
2004 Fall Semester
Term Project (Due Dec 31)

In this course, you are supposed to complete ...
Ayse ÖzTürk -Bioinformatics Chapter 10 in Data Mining: Multimedia, Softcomputing, Bioinformatis
Hüseyin Hakkoyma...
22         Dagci)
          - Semantic Web Mining for Counter-Terrorism
          - Web Usage and Struc...
Data Mining: Opportunities and Challenges
                 by John Wang (ed)                                              ...
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CENG 452 Data Mining (A. Kurt).doc


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CENG 452 Data Mining (A. Kurt).doc

  1. 1. 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. Outputs: • 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
  2. 2. Subjects: Ayse ÖzTürk -Bioinformatics Chapter 10 in Data Mining: Multimedia, Softcomputing, Bioinformatis Hüseyin Hakkoymaz -Weather Forecast Project Investigative Data Mining for Security and Criminal Detection Introduction 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) Chapter - Processes and Techniques for Web Data Mining (Suat Mercan) 10 Chapter - Mining Databases on the Web (Yemen Açıkgöz) 11 Chapter - Information Retrieval and Web Data Mining (Yemen Açıkgöz) 12 Chapter - Information Management and Web Data Mining (A Aslan) 13 Chapter - Semantic Web Mining (A Aslan) 14 Chapter - Mining Usage Patterns and Structure on the Web (M E Bodur) 15 Chapter - Prototypes, Products, and Standards for Web Data Mining (M E Bodur) 16 Chapter - Some Applications for Web Mining (M A Şengül) 17 Chapter Some Information on Terrorism, Security Threats, and Protection Measures - 18 (M A Şengül) Chapter - Web Data Mining for Counter-Terrorism (A Ertekin) 19 Chapter - Mining Web Databases for Counter-Terrorism (A Ertekin) 20 Chapter - Information Retrieval and Web Mining for Counter-Terrorism (A Dagci) 21 Chapter - Information Management and Web Mining for Counter-Terrorism (A
  3. 3. 22 Dagci) Chapter - Semantic Web Mining for Counter-Terrorism 23 Chapter - Web Usage and Structure Mining for Counter-Terrorism 24 Chapter - National Security, Privacy, Civil Liberties, and Web Mining 25 Chapter - Revisiting Security Threats with Respect to Web Mining 26 Chapter - E-Commerce, Business Intelligence, and Counter-Terrorism 27 Chapter - Summary and Directions 28
  4. 4. Data Mining: Opportunities and Challenges by John Wang (ed) ISBN:1591400511 Idea Group Publishing © 2003 (468 pages) In this text, an international team of 44 data mining experts specifically explore new methodologies or examine case studies in this new and multi-disciplinary topic. Table of Contents Back Cover Table of Contents Data Mining—Opportunities and Challenges Preface Chapter I - A Survey of Bayesian Data Mining Control of Inductive Bias in Supervised Learning Using Evolutionary Computation— Chapter II - A Wrapper-Based Approach Chapter III - Cooperative Learning and Virtual Reality-Based Visualization for Data Mining Chapter IV - Feature Selection in Data Mining (Not Easy Reading) Parallel and Distributed Data Mining through Parallel Skeletons Chapter V - and Distributed Objects Chapter VI - Data Mining Based on Rough Sets Chapter VII - (Easy Reading) The Impact of Missing Data on Data Mining Chapter VIII - Mining Text Documents for Thematic Hierarchies Using Self-Organizing Maps Chapter IX - (Easy Reading) The Pitfalls of Knowledge Discovery in Databases and Data Mining Chapter X - Maximum Performance Efficiency Approaches for Estimating Best Practice Costs Chapter XI - Bayesian Data Mining and Knowledge Discovery Chapter XII - (Easy Reading) Mining Free Text for Structure Chapter XIII - (Easy Reading)Query-By-Structure Approach for the Web Financial Benchmarking Using Self-Organizing Maps—Studying the International Chapter XIV - Pulp and Paper Industry Chapter XV - Data Mining in Health Care Applications Chapter XVI - Data Mining for Human Resource Information Systems Chapter XVII - Data Mining in Information Technology and Banking Performance Chapter - Social, Ethical and Legal Issues of Data Mining XVIII Chapter XIX - Data Mining in Designing an Agent-Based DSS Critical and Future Trends in Data Mining—A Review of Key Data Mining Chapter XX - Technologies/Applications