Knowledge representation is perhaps the most central problem confronting artificial intelligence. Expert systems need knowledge of their domain of expertise in order to function properly. Computer vlslOn systems need to know characteristics of what they are "seeing" in order to be able to fully interpret scenes. Natural language systems are invaluably aided by knowledge of the subject of the natural language discourse and knowledge of the participants in the discourse. Knowledge can guide learning systems towards better understanding and can aid problem solving systems in creating plans to solve various problems. Applications such as intelligent tutoring. computer-aided VLSI design. game playing. automatic programming. medical reasoning. diagnosis in various domains. and speech recogOltlOn. to name a few. are all currently experimenting with knowledge-based approaches. The problem of knowledge representation breaks down into several subsidiary problems including what knowledge to represent in a particular application. how to extract or create that knowledge. how to represent the knowledge efficiently and effectively. how to implement the knowledge representation scheme chosen. how to modify the knowledge in the face of a changing world. how to reason with the knowledge. and how tc use the knowledge appropriately in the creation of the application solution. This volume contains an elaboration of many of these basic issues from a variety of perspectives.

ThePaci?c-AsiaConferenceonKnowledgeDiscoveryandData Mining hasbeen held every year from 1997. PAKDD 2009, the 13th in the series, was held in Bangkok, Thailand during April 27-30, 2008. PAKDD is a major inter- tional conference in the areas of data mining (DM) and knowledge discovery in database (KDD). It provides an international forum for researchers and ind- try practitioners to share their new ideas, original research results and prac- cal development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acqui- tion and automatic scienti?c discovery, data visualization, causal induction and knowledge-based systems. ForPAKDD2009, wereceived338researchpapersfromvariouscountriesand regions in Asia, Australia, North America, South America, Europe, and Africa. Every submission was rigorously reviewed by at least three reviewers with a doubleblindprotocol.Theinitialresultswerediscussedamongthereviewersand ?nally judged by the ProgramCommittee Chairs. When there was a con?ict, an additionalreviewwasprovidedbytheProgramCommitteeChairs.TheProgram Committee members were deeply involved in the highly selective process. As a result, only 39 papers (approximately 11.5% of the 338 submitted papers) were accepted as regular papers, 73 papers (21.6% of them) were accepted as short papers

The Fourth International Conference on Advanced Data Mining and Applications (ADMA 2008) will be held in Chengdu, China, followed by the last three successful ADMA conferences (2005 in Wu Han, 2006 in Xi'an, and 2007 Harbin). Our major goal of ADMA is to bring together the experts on data mining in the world, and to provide a leading international forum for the dissemination of original research results in data mining, including applications, algorithms, software and systems, and different disciplines with potential applications of data mining. This goal has been partially achieved in a very short time despite the young age of the conference, thanks to the rigorous review process insisted upon, the outstanding list of internationally renowned keynote speakers and the excellent program each year. ADMA is ranked higher than, or very similar to, other data mining conferences (such as PAKDD, PKDD, and SDM) in early 2008 by an independent source: cs-conference-ranking. org. This year we had the pleasure and honor to host illustrious keynote speakers. Our distinguished keynote speakers are Prof. Qiang Yang and Prof. Jiming Liu. Prof. Yang is a tenured Professor and postgraduate studies coordinator at Computer Science and Engineering Department of Hong Kong University of Science and Technology. He is also a member of AAAI, ACM, a senior member of the IEEE, and he is also an as- ciate editor for the IEEE TKDE and IEEE Intelligent Systems, KAIS and WI Journals.

This book constitutes the refereed proceedings of the Second International Conference on Rough Sets and Knowledge Technology, RSKT 2007, held in Toronto, Canada in May 2007 in conjunction with the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2007, both as part of the Joint Rough Set Symposium, JRS 2007.

The 67 revised full papers papers presented together with 3 invited papers were carefully reviewed and selected from a total of 319 general submissions to the JRS 2007 symposium. The papers are organized in topical sections on rough set foundations, multiple criteria decision analysis, biometrics, kansei engineering, autonomy-oriented computing, soft computing in bioinformatics, ubiquitous computing and networking, rough set algorithms, knowledge representation and reasoning, genetic algorithms, and rough set applications.

Knowledge representation is perhaps the most central problem confronting artificial intelligence. Expert systems need knowledge of their domain of expertise in order to function properly. Computer vlslOn systems need to know characteristics of what they are "seeing" in order to be able to fully interpret scenes. Natural language systems are invaluably aided by knowledge of the subject of the natural language discourse and knowledge of the participants in the discourse. Knowledge can guide learning systems towards better understanding and can aid problem solving systems in creating plans to solve various problems. Applications such as intelligent tutoring. computer-aided VLSI design. game playing. automatic programming. medical reasoning. diagnosis in various domains. and speech recogOltlOn. to name a few. are all currently experimenting with knowledge-based approaches. The problem of knowledge representation breaks down into several subsidiary problems including what knowledge to represent in a particular application. how to extract or create that knowledge. how to represent the knowledge efficiently and effectively. how to implement the knowledge representation scheme chosen. how to modify the knowledge in the face of a changing world. how to reason with the knowledge. and how tc use the knowledge appropriately in the creation of the application solution. This volume contains an elaboration of many of these basic issues from a variety of perspectives.