Announcements

[June 11 2009] Final Report Due (June 11th, Midnight)

[June 11 2009] Project Presentation Demo, CS Conference Room, 3-6pm (HFH 1132)

[June 2 2009] Three hours lecture/presentation to cover Week 6 (05/07/2009) OLAP and Data Cube and Week 10 (06/02/2009) Software Data Analysis

[May 7 2009] No Class, will be rescheduled.

[May 5 2009] Two student presentations.  Social Network I and II.

[April 30 2009] No Class, replaced by the April 22 lecture.

[April 22 2009] We will have an additional lecture on April 22, Wed, 9:30AM-11AM, Phelps 1401 (Prof. Zhao’s Social Network class) , No student presentation! 

[April 14 2009] Before the class, please upload your first review in the course twiki site under the review directory. The first review includes the two papers that are going to be presented in Week 3.  Each paper review should have 1 page, summarizing the paper's main contribution, its strength and weakness, and possible improvement.  If you have any problem to use twiki, please contact Arijit during his TA office.

[April 09 2009] (1) Ashish will volunteer to present an interesting SIGCOMM'08 paper,  What's Going On? Learning Communication Rules in Edge Networks,  which is related to association rule mining. (2) Paper selection is due.

[April 04 2009] The course reading list is ready,  link.  The course twiki site is set up, link, please register, get used to the twiki environment, select the paper you are going to present,  and add your name into the twiki presentation schedule table (Due on 04/09/2009).  Two students could form a team to present one paper.  If you have any problem to use twiki, please contact Arijit during his TA office.

[April 01 2009] Twiki site was delayed.  We have a temporary website for slides and projects http://www.cs.ucsb.edu/~xyan/private/ user name and password will be announced in the class on April 2.

Abstract: Advanced course which introduces data mining concepts, principles and algorithms. Topics include association analysis, classification, clustering, sequential pattern analysis, stream data mining, mining complex types of data, multi-relational data mining, spatiotemporal data mining, social network analysis, text mining, link analysis, data mining applications (bioinformatics, security, the Web, software engineering, etc.) and trends in data mining.

Each student is expected to write reviews (two papers) every week, present one paper and lead the discussion (45 mins) after his/her presentation, and complete a project.

Prerequisites: CS130