PPT-WEB FORUM MINING BASED ON USER SATISFACTION

Page 1 WEB FORUM MINING BASED ON USER SATISFACTION By Suresh Pokharel Information and Communications Technologies Asian Institute of Technology Committee Dr Sumanta Guha . Global markets remain weak following the global 64257nancial crisis commodity prices have fallen and shareholders are pushing for leaner operations and greater returns on investment At the same time the stakeholder landscape for mining and metals co FY 2010. Wendy L. Gagnier, P.E.. BRIDGEWare Project Manager. BRIDGEWare Task Force Members. Virtis/Opis. Tim Armbrecht – IL DOT & BW Chair. Dean Teal – KS DOT. Rebecca Curtis – MI DOT. Bryan Silvis – VA DOT. Chapter 1. Kirk Scott. Iris . virginica. 2. Iris . versicolor. 3. Iris . setosa. 4. 1.1 Data Mining and Machine Learning. 5. Definition of Data Mining. The process of discovering patterns in data.. (The patterns discovered must be meaningful in that they lead to some advantage, usually an economic one.). Graphs are a flexible & unifying model. Scalable similarity searches through novel index structure. Mining of significant fragments in collections. Classification of compounds based on significant fragments . Subproblems. . Meliha. . Yetisgen-Yildiz. From last week’s discussion. Presentation. Schedule. : . http. ://faculty.washington.edu/melihay/. MEBI591C.htm. 50 . minutes . presentation+discussion+question. : . A . Case Study of Hospital Information . Systems. College of Business and Finance. Professor Mukhtar AL-. Hashimi. and . Mishleen. M. . Aqleh. . Introduction. Innovative technologies have touched all aspect of our life, . User Behavior Modeling and Search Personalization. *. Hongning . Wang. Department of Computer Science. University of Illinois at Urbana-Champaign. Urbana IL, 61801 USA. wang296@illinois.edu. *work is done when visiting Microsoft Research. CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Web Mining. Today. Overview of Web Data Mining. Web Content Mining / Text Mining. Web Usage Mining. Web Personalization. A tutorial. Gary M. Weiss. Fordham University. gweiss@cis.fordham.edu. These slides available from http://storm.cis.fordham.edu/~gweiss/presentations.html. What is a Smart Phone?. What is a smart phone and what does it do? What devices can it replace? . Gary M. Weiss. Fordham University. gweiss@cis.fordham.edu. Background and Motivation. Smart phones are ubiquitous. As of 4. th. quarter 2010 outpaced PC sales. We carry them everywhere at almost all times. F Factor (Middle and High School Only, Advisor Led). Introduction and Rules. Back in the Day (In the Future Elementary). Similarity Activities (Time: Choose 1 or 2). Forum Bingo. Common Interests/Values. interesting . and . useful. information from Web . content. and . usage . data. What is Web Mining?. Web mining is . a data . mining . technique . to extract knowledge from . web data. . . Web data includes : . Meeting. HLRS October . 2010. Important Dates For Your Calendar . FUTURE HPC USER FORUM MEETINGS:. International Meetings:. HLRS/University of Stuttgart, October 7/8, 2010. SARA Computing & Networking Services, Amsterdam, October . Bamshad Mobasher. DePaul University. 2. From Data to Wisdom. Data. The raw material of information. Information. Data organized and presented by someone. Knowledge. Information read, heard or seen and understood and integrated.