Statistical implicative analysis is a data analysis method created by Regis Gras almost thirty years ago which has a significant impact on a variety of areas ranging from pedagogical and psychological research to data mining. Statistical implicative analysis (SIA) provides a framework for evaluating the strength of implications; such implications are formed through common knowledge acquisition techniques in any learning process, human or artificial. This new concept has developed into a unifying methodology, and has generated a powerful convergence of thought between mathematicians, statisticians, psychologists, specialists in pedagogy and last, but not least, computer scientists specialized in data mining. This volume collects significant research contributions of several rather distinct disciplines that benefit from SIA. Contributions range from psychological and pedagogical research, bioinformatics, knowledge management, and data mining.

ThePaci?c-AsiaConferenceonKnowledgeDiscoveryandDataMining(PAKDD) has been held every year since 1997. PAKDD 2008, the 12th in the series, was heldatOsaka, JapanduringMay20-23,2008.PAKDDisaleadinginternational conference in the area of data mining. It provides an international forum for - searchers and industry practitioners to share their new ideas, original research results, and practical development experiences from all KDD-related areas - cluding data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition, automatic scienti?c discovery, data visualization, causal induction, and knowledge-based systems. This year we received a total of 312 research papers from 34 countries and regions in Asia, Australia, North America, South America, Europe, and Africa. Every submitted paper was rigorously reviewed by two or three reviewers, d- cussed by the reviewers under the supervision of an Area Chair, and judged by the Program Committee Chairs. When there was a disagreement, the Area Chair and/or the Program Committee Chairs provided an additional review. Thus, many submissions were reviewed by four experts. The Program Comm- tee members were deeply involved in a highly selective process. As a result, only approximately11.9%ofthe312submissionswereacceptedaslongpapers,12.8% of them were accepted as regular papers, and 11.5% of them were accepted as short papers

Statistical implicative analysis is a data analysis method created by Regis Gras almost thirty years ago which has a significant impact on a variety of areas ranging from pedagogical and psychological research to data mining. Statistical implicative analysis (SIA) provides a framework for evaluating the strength of implications; such implications are formed through common knowledge acquisition techniques in any learning process, human or artificial. This new concept has developed into a unifying methodology, and has generated a powerful convergence of thought between mathematicians, statisticians, psychologists, specialists in pedagogy and last, but not least, computer scientists specialized in data mining.

This volume collects significant research contributions of several rather distinct disciplines that benefit from SIA. Contributions range from psychological and pedagogical research, bioinformatics, knowledge management, and data mining.

This book constitutes the refereed proceedings of the 10th International Conference on Discovery Science, DS 2007, held in Sendai, Japan, in October 2007, co-located with the 18th International Conference on Algorithmic Learning Theory, ALT 2007.

The 17 revised long papers and the 10 revised regular papers presented together with 5 invited papers were carefully reviewed and selected from 55 submissions. The papers cover all issues in the area of development and analysis of methods for intelligent data analysis, knowledge discovery and machine learning, as well as their application to scientific knowledge discovery.

This volume contains the papers presented at the 7th International Conference on Discovery Science (DS 2004) held at the University of Padova, Padova, Italy, during 2-5 October 2004. The main objective of the discovery science (DS) conference series is to p- videanopenforumforintensivediscussionsandtheexchangeofnewinformation amongresearchersworkingintheareaofdiscoveryscience. Ithasbecomeagood custom over the years that the DS conference is held in parallel with the Int- national Conference on Algorithmic Learning Theory (ALT). This co-location has been valuable for the DS conference in order to enjoy synergy between c- ferences devoted to the same objective of computational discovery but from di?erent aspects. Continuing the good tradition, DS 2004 was co-located with the 15th ALT conference (ALT 2004) and was followed by the 11th Symposium on String Processing and Information Retrieval (SPIRE 2004). The agglome- tion of the three international conferences together with the satellite meetings was called Dialogues 2004, in which we enjoyed fruitful interaction among - searchers and practitioners working in various ?elds of computational discovery. The proceedings of ALT 2004 and SPIRE 2004 were published as volume 3244 of the LNAI series and volume 3246 of the LNCS series, respectively. The DS conference series has been supervised by the international ste- ing committee chaired by Hiroshi Motoda (Osaka University, Japan). The other members are Alberto Apostolico (University of Padova, Italy and Purdue U- versity, USA), Setsuo Arikawa (Kyushu University, Japan), Achim Ho?mann (UNSW, Australia), Klaus P. Jantke (DFKI, Germany), Masahiko Sato (- oto University, Japan), Ayumi Shinohara (Kyushu University, Japan), Carl H.

This volume contains the papers selected for presentation at the 14th International Symposium on Methodologies for Intelligent Systems, ISMIS 2003, held in Maebashi City, Japan, 28-31 October, 2003. The symposium was organized by the Maebashi Institute of Technology in co-operation with the Japanese Society for Artificial Intelligence. It was sponsored by the Maebashi Institute of Technology, Maebashi Convention Bureau, Maebashi City Government, Gunma Prefecture Government, US AFOSR/AOARD, the Web Intelligence Consortium (Japan), Gunma Information Service Industry Association, and Ryomo Systems Co., Ltd. ISMIS is a conference series that was started in 1986 in Knoxville, Tennessee. Since then it has been held in Charlotte (North Carolina), Knoxville (Tennessee), Turin (Italy), Trondheim (Norway), Warsaw (Poland), Zakopane (Poland), and Lyon (France). The program committee selected the following major areas for ISMIS 2003: active media human-computer interaction, autonomic and evolutionary computation, intelligent agent technology, intelligent information retrieval, intelligent information systems, knowledge representation and integration, knowledge discovery and data mining, logic for artificial intelligence, soft computing, and Web intelligence.