ISMT527 - SPRING 2003 DATA MINING TOOLS AND APPLICATIONS

Transcription

1 ISMT527 - SPRING 2003 DATA MINING TOOLS AND APPLICATIONS Class Venue: Room 4116 Lecture Time: Saturday 18:30-21:50 Course s web page: (logon with you ID/student ID) Instructor: Prof. Oliver Yue Zhao Tel: Office: oliver@ust.hk Office Hours: by appointment Teaching Assistant: Philip Yuen Tel: Office imytk@ust.hk Office Hours: by appointment COURSE DESCRIPTION This course introduces the fundamental issues in data mining with emphasis on business applications and expose students to the theory and practice with an emphasis on Customer Relation Management (CRM). The will be a practical course which covers many aspects of implementation a data mining project in the real world setting: data source, data processing, data mining infrastructure, (data warehousing), data mining tool overview and selection, introduction of data mining algorithms (association mining, decision tree, neural network... ) and model selection, finally cover data mining implementation and application. This course will also provide students with hands-on experience on popular data mining tools in the marketplace. MATERIALS a. Require Readings Data Mining A Tutorial-Based Primer Authors: Richard J. Roiger & Michael W. Geatz Publisher: Addison-Wesley (2003) b. Recommended Readings Data Mining: Concepts and Techniques by Jiawei Han and Micheline Kamber, Morgan Kaufmann Publishers, 2001, ISBN: Online Reading package (available online) Data Mining Techniques: For Marketing, Sales and Customer Support by Michael Berry and Gordon Linoff, John Wiley and Sons, 1997, ISBN: Mastering Data Mining: The Art and Science of Customer relationship Management by Michael Berry and Gordon Linoff, John Wiley and Sons, 2000, ISBN: Data Mining Solutions: Methods and Tools for Solving Real-World Problems Christopher Westphal, Teresa Blaxton Publisher: Wiley, John & Sons, Incorporated. ISBN: Building Data Mining Applications for CRM by Alex Berson, Kurt Thearling, Stephen J. Smith Publisher: McGraw-Hill Professional Pub ISBN:

2 INSTRUCTIONAL METHODS USED The course uses lectures, hand on data mining project, discussions of examples/ applications, assigned readings, exercises, team projects, and in-class exams to achieve the learning objective. GRADING POLICY Your final grade for the course will be determined on the basis of your performance on the class participation, the two exams, and the final project. The weights and detail explanations for each of these components are as follows. On/offline Class Participation 30% Project: Individual Proposal 40% Project: Group Report 30% a. Online and offline Class participation Each student will also be expected to participate in class discussions. Class participation does not solely mean responding to questions: you are also urged to ask questions that lead to fruitful discussion. Questions and comments from the students during the lecture are strongly encouraged. This will make things a lot more interesting for everyone (include myself). The lectures are much more effective when participants are prepared and active, and peer feedback is something you can never get from the textbook. You are encouraged to participate online discussion at course web site. You are kindly requested to share your questions, comments, and insights on the concept, hand on project and the final corporate proposal, as you work on them, by posting them on an electronic bulletin board on WebCT. So doing is likely to contribute the learning experience for all of us, while also creating a mutually supportive atmosphere for the course. DETAILS ON OFFLINE PARTICIPATION Your participation in electronic class discussions via posts on the electronic bulletin board on WebCT would be greatly appreciated. It is an integral part of the learning experience, because normal (i.e. face-to-face) classroom interaction will be missing from the on-line portion of the course for some students. Thus, the purpose of the e- participation grade component is to try and simulate as much classroom interaction as possible, within the constraints of an on-line environment. Thus, you are kindly requested to make frequent contributions to this interaction by sharing your comments, questions and insights on the final project and corporate proposal, as you work on them, by posting them to the electronic bulletin board on WebCT. We will try to maintain at least two separate discussion threads on the bulletin board. One relating to the final project and the other relating to the corporate proposal. Due to (potential) confidentiality concerns, it is probable that we will have fewer contributions to the corporate proposal thread. For the final project thread, we can expect a lively and animated discussion (and perhaps some whining too!). At the end of the course I will evaluate both the frequency and content of your electronic contributions to the bulletin board to assess your e-participation grade. 2

3 Examples of positive e-participation: thoughtful questions; thoughtful answers to those questions (double points); tips on using Clementine efficiently; pointers on useful mining strategies; sharing any information that is likely to be useful to others; offering moral support. Examples of negative e-participation: excessive whining; posting specific and detailed answers to the final project questions; being unduly critical of somebody else s post. e. Final project and Team Report The students will form to group (5 or 6 students each group) during the first class. You are supposed to sit and work together for the course discussion and help each other on the hand on project. The objective underlying the project is to enable students to assimilate classroom material in a real-life situation. The main purpose is to leverage your newly acquired data mining expertise by showing how data mining concepts and tools can be used in your current (or future) organization. It is recommended that each team member will find the real world problem inside your organization or find a real case in the industry you are interesting by yourself. An project proposal will be submitted individually (see the due date on the course schedule ). There is no page limit to your report. A suggested length, excluding references, appendixes and other attachments would be around 6 to 12 pages (single space), depending upon your preferences for font size. Then team will work together to discussion and pick up the most challenge or interesting one to do a deep study and prepare the presentation to the rest of class. The team report of project should be set-up as a verbal presentation (with a handout/notes). Each team will presentation their work at the last session of the class. Each team will allow 15 minutes for the presentation and Q&A. A hard and soft copy of power point presentation is due on the same day of presentation (Week 8). Recommended Coverage for individual proposal and team presentation Executive summary Background of organization (including problem and opportunity) Description of data source available for mining in terms of what type of information they contain. Dataset merging/consolidation issues. What would be the specific objective of the data mining analyses? What would be the applicable data mining concepts and theories? Which data mining tools do you choose? How could the data mining software be used to implement the data mining analyses? What are the nuggets of actionable marketing information that would be discovered? Demo the results of sample data set (optional). Expected returns to the company from the investment in data mining expertise. Feel free to add/delete topics, create headings, sub-headings as needed. Use your own format in setting up the proposal. But, tailor your presentation to suit a specific current (or a hypothetical future) decision situation as regards the data source available for 3

4 use, applicable data mining concepts and theories, analytical procedures, and the expected nuggets of actionable information to be mined. Be as precise as you can about the type of dataset(s) being used and the expected quantitative findings. LAB AND DATA MINING SOFTWARE An important part of the course is the use of Windows-based software for the analysis and management of data sets in the computer lab. Because many topics and concepts in data mining are learned most efficiently through hands-on work with data sets, we will spend a fair amount of time (during the lecture) analyzing and mining business data sets with contemporary software. You will learn how to use different data mining methods on a dataset. The use of this software will be demonstrated during class meetings. You are required to become familiar with the aspects of the software covered in class (including interpretation of output). The goal is not to master all the features and intricacies of specific software packages (which you may well make little to no use of again), but rather to gain a better understanding of (a) how data mining is applied and (b) what is involved in data mining projects (including the steps of the data mining process). The primary software packages used in the course are Clementine (version 7.0; which available in the Microsoft Net lab (4116). You are NOT required to purchase the Clementine and manuals for the software used in the course (although you are welcome to do so). You can also purchase a student version of Clementine at cost of ($2850, the commercial version we have in lab cost $37,000 annually). Instead of purchasing manuals, you may refer to the online help provided with the software as well as the documentation (in pdf format) for Clementine provided on the computers in Microsoft Net Lab (I will make it available at course website). Another small scale data mining tools Intelligent Data Analyzer (ida) is also available when you purchase the text book. HOW TO DO WELL IN THIS COURSE The students who get the most out of this course will be the ones who put in the most effort. If you want to do well, attend all the lectures, read the assigned sections of the book/papers before and after the lecture, and start early on your projects. Your class participation also makes big difference. Try your best let me know your name and your face! Use the electronic bulletin board on WebCT as an important shared resource, both to seek advice from others, and to contribute your own insights, so that others may benefit from them. I will be more than happy to help you if you have any question or problem. However, you owe it to yourself to get help. I will hold extensive office hours. Contact me anytime for an appointment. It is your responsibility to check the web site before each class and check your account regularly. I sincerely want all of you to enjoy the class and do well in the class. 4

5 COURSE IMPROVEMENT Continuous improvement requires continuous communication between all stakeholders in an organization. The success of this course depends upon our communication. Please let the instructor know throughout the semester any suggestions for improvement. It is also possible that you can sent me an anonymous (without release your real and name) suggestion to improve classroom and teaching effectiveness at: The instructor will use your feedback to identify appropriate changes to the course. Please note that it might not be feasible to incorporate some changes during the current semester. 5