1. Computing & Information Sciences Kansas State University CIS 690 Data Mining in Mobile and Cloud Computing Environments William H. Hsu, Computing and Information Sciences Shih-Hsiung Chou, Industrial and Manufacturing Systems Engineering Kansas State University KSOL course page: http://bit.ly/a68KuLhttp://bit.ly/a68KuL Course web site: http://www.kddresearch.org/Courses/CIS690http://www.kddresearch.org/Courses/CIS690 Instructor home page: http://www.cis.ksu.edu/~bhsuhttp://www.cis.ksu.edu/~bhsu Reading for Next Class: Syllabus and Introductory Handouts Instructions for Labs 0 – 1 Han & Kamber 2 e, Sections 1.1 – 1.4.3 (pp. 1 – 25), 6.1 (pp. 285 – 289) Data Mining in Mobile and Cloud Computing Environments: Course Organization and Survey Lecture 0 of 27: Part A – Course Organization

2. Computing & Information Sciences Kansas State University CIS 690 Data Mining in Mobile and Cloud Computing Environments Course Administration Course Page (KSOL): http://bit.ly/a68KuLhttp://bit.ly/a68KuL Class Web Page: www.kddresearch.org/Courses/CIS690www.kddresearch.org/Courses/CIS690 Instructional E-Mail Addresses – Best Way to Reach Instructor  CIS690TA-L@listserv.ksu.edu (always use this to reach instructor and TA) CIS690TA-L@listserv.ksu.edu  CIS690-L@listserv.ksu.edu CIS690-L@listserv.ksu.edu Instructor: William Hsu, Nichols 324C  Office phone: +1 785 532 7905; home phone: +1 785 539 7180  IM: AIM/MSN/YIM hsuwh/rizanabsith, ICQ 28651394/191317559, Google banazir  Office hours: after class Mon/Wed/Fri; other times by appointment Graduate Teaching Assistant: To Be Announced  Office location: Nichols 124 (CIS Visualization Lab) & Nichols 218  Office hours: to be announced on class web board Grading Policy: Overview  Midterm exam: 15%  Homework: 15%  Term project: 50%  Labs: 20% (1% each; see calendar)

3. Computing & Information Sciences Kansas State University CIS 690 Data Mining in Mobile and Cloud Computing Environments Course Policies Letter Grades  15% graduations (85+%: A, 70+%: B, etc.)  Cutoffs may be more lenient, but a) never higher and b) seldom much lower Grading Policy  Exams: midterm (in-class, open-book/notes) 15%  Homework: 15% (2 written, 2 programming, 2 mixed; drop lowest 2, 3% each)  Term project (including proposal, interim, final reports): 50%  Labs (upload solutions to K-State On-Line file dropbox): 20% Late Homework Policy  Allowed only in case of medical excusal  All other late homework: see drop policy Attendance Policy  Absence due to travel or personal reasons: e-mail CIS690TA-L in advance  See instructor, Office of the Dean of Student Life as needed Honor System Policy: http://www.ksu.edu/honor/http://www.ksu.edu/honor/  On plagiarism: cite sources, use quotes if verbatim, includes textbooks  OK to discuss work, but turn in your own work only  When in doubt, ask instructor

4. Computing & Information Sciences Kansas State University CIS 690 Data Mining in Mobile and Cloud Computing Environments Course Content Management System (CMS)  http://www.kddresearch.org/Courses/CIS690 http://www.kddresearch.org/Courses/CIS690  Lecture notes (MS PowerPoint 97-2010, PDF)  Homeworks (MS Word 97-2010, PDF)  Exam and homework solutions (MS PowerPoint 97-2010, PDF)  Class announcements (students’ responsibility) and grade postings Course Notes Online and at Copy Center (Required) Mailing List (Automatic): CIS690-L@listserv.ksu.eduCIS690-L@listserv.ksu.edu  Homework/exams (before uploading to CMS, KSOL), sample data, solutions  Class participation  Project info, course calendar reminders  Dated research announcements (seminars, conferences, calls for papers) LISTSERV Web Archive  http://listserv.ksu.edu/archives/cis690-l.html http://listserv.ksu.edu/archives/cis690-l.html  Stores e-mails to class mailing list as browsable/searchable posts Class Resources

5. Computing & Information Sciences Kansas State University CIS 690 Data Mining in Mobile and Cloud Computing Environments Recommended Text Witten, I. H. & Frank, E. (2006). Data Mining: Practical Machine Learning Tools and Techniques, second edition. San Francisco, CA, USA: Morgan Kauffman. Other References [on Reserve in Main or CIS Library] Han, J. & Kamber, M. (2006). Data Mining: Concepts and Techniques, second edition. San Francisco, CA, USA: Morgan Kauffman. Mitchell, T. M. (1997) Machine Learning. New York, NY, USA: McGraw-Hill. Tan, P.-N., Steinbach, M., & Kumar, V. (2006). Introduction to Data Mining. Reading, MA, USA: Addison-Wesley. Textbook and Recommended References Mitchell (1997) Witten & Frank 2 e Tan et al. (2006) 1 st edition (outdated) Han & Kamber 2 nd edition

6. Computing & Information Sciences Kansas State University CIS 690 Data Mining in Mobile and Cloud Computing Environments Both Courses  Proficiency in high-level programming language (C++/C#, Java, Python, etc.)  Required: course in data structures  Recommended: discrete mathematics, probability  At least 80 hours for semester (up to 120 depending on term project)  Textbook – Data Mining: Concepts and Techniques, 2 e, Han & Kamber (2006)  Reserve texts: Mitchell’s Machine Learning, several other outside references CIS 690 Data Mining in Mobile and Cloud Computing Environments  Fresh background in symbolic logic, discrete math (sets, relations, counting)  Some background assumed in linear algebra, calculus  New topics: classification/regression, association, optimization, clustering  “Mathematical maturity”: ready to learn more CIS 798 Topics in Computer Science  Recommended: two programming courses  Read up on heuristic search, games, constraints, knowledge representation  AI programming experience helps (background lectures as needed)  Watch advanced topics lectures; see list before choosing project topic Background Expected

7. Computing & Information Sciences Kansas State University CIS 690 Data Mining in Mobile and Cloud Computing Environments Syllabus [1]: First Half of Course

8. Computing & Information Sciences Kansas State University CIS 690 Data Mining in Mobile and Cloud Computing Environments Syllabus [2]: Second Half of Course

9. Computing & Information Sciences Kansas State University CIS 690 Data Mining in Mobile and Cloud Computing Environments Basics: First Two Weeks (Hours 2 – 9 of Course)  Review of mathematical foundations: set theory, discrete math, probability  Types of machine learning algorithms  Combinatorial analysis: mappings and counting  Bayesian classification Bayesian Inference  Hour 3: association rules, statistical evaluation  Hours 6 – 10: Naïve Bayes, classification in R  Hours 15 – 18: clustering, Expectation-Maximization (EM) Other Math Topics to be Covered  Information theory: decision tree induction, rule induction  Basic statistical hypothesis testing  Frequent itemsets: association rule mining  Convex optimization: constraints, linear and quadratic programming (QP)  Distance measures: clustering  Logic: propositional, first-order, resolution Math Background To Be Covered

10. Computing & Information Sciences Kansas State University CIS 690 Data Mining in Mobile and Cloud Computing Environments Computing Platform: Mobile/Cloud Environments Android  Operating system: modified Linux  For mobile devices (Motorola Droid, HTC Incredible, etc.)  Android, Inc. & Open Handset Alliance  Software development kit: download from http://developer.android.com/sdk/http://developer.android.com/sdk/ Software Environment for the Advancement of Scholarly Research  Originally developed for compute clusters  Adapted for cloud computing environments  SEASR – overall environment: http://seasr.orghttp://seasr.org  Meandre – data mining flows: http://seasr.org/meandre/http://seasr.org/meandre/  © 2005 – present, National Center for Supercomputing Applications (NCSA)

11. Computing & Information Sciences Kansas State University CIS 690 Data Mining in Mobile and Cloud Computing Environments Computing Platform: Data Mining Software Waikato Environment for Knowledge Analysis (WEKA)  Data mining package  Most popular machine learning and data mining software at present  Download from http://www.cs.waikato.ac.nz/ml/weka/http://www.cs.waikato.ac.nz/ml/weka/ R Interpreter  R: popular programming language for computational statistics  Used for data mining implementations  Comprehensive R Archive Network (CRAN): http://cran.r-project.orghttp://cran.r-project.org Apache Hadoop  Java software framework  Data-intensive distributed applications  Inspired by Google MapReduce and Google File System (GFS)

12. Computing & Information Sciences Kansas State University CIS 690 Data Mining in Mobile and Cloud Computing Environments About Project Proposals Proposals  About 1-2 pages; due at end of second week of course, one revision allowed  Team projects: up to 2 people  Contents: at least one paragraph on each of –1. Problem statement: describe task, objectives, purpose –2. Background: survey related work and applicable approaches –3. Methodology: describe planned approach –4. Evaluation criteria: how will performance be assessed? –5. Milestones: what will be done, when? Post Questions and Drafts to Class Mailing List