Course Update Information Course ObjectivesTextbook Data Mining: Introductory and Advanced Topics. by Margaret H. DunhamScheduleAssignments and GradingComputer Science Mount Holyoke College CS341 Data Mining, Spring 2007Instructor: Class Meets:Classroom:Office Hours:Office: Email:Professor Xiaoyan Li M, W 2:40 AM-3:55 PMKendade HallT,Th 10:00-11:00 AMClapp [email protected] Course Update Information - January 29. First Day of Class. Welcome all! Course ObjectivesData Mining has become one of the most exciting and fastest growing fields in computer science. Data Mining refers to various techniques which can be used to uncover hidden information from a database. The data to be mined may be complex, multimedia data including text, graphics, video, audio and bioinformatics data. Data Mining has evolved from several areas including: databases, artificial intelligence, machine learning, pattern recognition, multimedia information retrieval, and can be applied to the exploration of hidden information from web, video, and bioinformatics data. This course is designed to provide senior undergraduate students with introductory of data mining concepts and tools. In addition, related concepts such as information retrieval, web mining and bioinformatics will be covered.Textbook Data Mining: Introductory and Advanced Topics. by Margaret H. DunhamScheduleThe following schedule is based on spring 2007 academic calendar: Date Planned Lecture Topics Read/Assign/Exam/labJan 29 (M)Jan 31 (W)IntroductionPart 1: Database Systems, Decision Support Systemsand WarehousingCh 1Ch 2Feb 5 (M)Feb 7 (W)Part 1: Information Retrieval, Questions Answering and Web SearchPart II: Data Mining techniques (I)Ch 2 (hw1)Ch 3Feb 12 Part II: Data Mining techniques (II) Ch 3 (hw2)(M)Feb 14 (W)Part III: Classification-Regression & Bayesian ClassificationCh 4Feb19 (M)Feb 21 (W)Part III: Classification – Distance-Based AlgorithmPart III: Classification - Decision Tree AlgorithmCh 4 (hw3)Ch 4Feb 26 (M)Feb 28 (W)Part III: Classification – Rule-Based AlgorithmReview sessionCh 4 (hw4)Mar 5 (M)Mar 7 (W)First in-class examPart IV: Clustering – Similarity and Distance MeasuresCh 5Mar 12 (M)Mar 14 (W)Part IV: Clustering – Hierarchical AlgorithmPart IV: Clustering – Partitional AlgorithmCh 5Ch 5 (hw 5)Mar 17-25Mid-semester breakMar 26 (M)Mar 28 (W)Part IV: PERL (I)Part IV: PERL (II)Apr 2 (M)Apr 4 (W)Part V: Project IdeasPart V: Project IdeasFinal projectApr 9 (M)Apr 11 (W)Part V: Project DiscussionPart V: Project DiscussionApr 16(M)Apr 18 (W)Part V: Project DiscussionPart V: Project DiscussionApr 23 (M)Apr 25 (W)Part V: Project DiscussionPart V: Project DiscussionApr 30 (M)May 2 (W)Part V: Project DiscussionPart V: Project DiscussionMay 7 (M)In-class PresentationReading daysMay 9-10May 11-15Assignments and GradingSee syllabus above for the tentative timetable for a schedule. There will be about 5 assignments taking up 25% of your final grade. There will be one midterm and one final project that contribute 20% and 40% of your final grade, respectively. The rest 15%goes to class participations. There will be no final examination. Policies: Students may discuss ideas together. But since each student get credits for her submissions, all solutions must be done separately by each student, and must not be shared. Communications: I would like the course to run smoothly and enjoyably. Feel free to let me know what you find good and interesting about the course. Let me know as soon as possible about the reverse. You may see me in my office during my hours or send me messages by e-mail. Copyright @ Xiaoyan Li, Mount Holyoke College, Spring