Data mining has emerged as one of the most active areas in information and c- munication technologies(ICT). With the boomingof the global economy,and ub- uitouscomputingandnetworkingacrosseverysectorand business,data andits deep analysis becomes a particularly important issue for enhancing the soft power of an organization, its production systems, decision-making and performance. The last ten years have seen ever-increasingapplications of data mining in business, gove- ment, social networks and the like. However, a crucial problem that prevents data mining from playing a strategic decision-support role in ICT is its usually limited decision-support power in the real world. Typical concerns include its actionability, workability, transferability, and the trustworthy, dependable, repeatable, operable and explainable capabilities of data mining algorithms, tools and outputs. This monograph, Domain Driven Data Mining, is motivated by the real-world challenges to and complexities of the current KDD methodologies and techniques, which are critical issues faced by data mining, as well as the ?ndings, thoughts and lessons learned in conducting several large-scale real-world data mining bu- ness applications. The aim and objective of domain driven data mining is to study effective and ef?cient methodologies, techniques, tools, and applications that can discover and deliver actionable knowledge that can be passed on to business people for direct decision-making and action-taking.

Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining."

Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from "data-centered pattern mining" to "domain driven actionable knowledge discovery" for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business.

Data mining has emerged as one of the most active areas in information and c- munication technologies(ICT). With the boomingof the global economy, and ub- uitouscomputingandnetworkingacrosseverysectorand business, data andits deep analysis becomes a particularly important issue for enhancing the soft power of an organization, its production systems, decision-making and performance. The last ten years have seen ever-increasingapplications of data mining in business, gove- ment, social networks and the like. However, a crucial problem that prevents data mining from playing a strategic decision-support role in ICT is its usually limited decision-support power in the real world. Typical concerns include its actionability, workability, transferability, and the trustworthy, dependable, repeatable, operable and explainable capabilities of data mining algorithms, tools and outputs. This monograph, Domain Driven Data Mining, is motivated by the real-world challenges to and complexities of the current KDD methodologies and techniques, which are critical issues faced by data mining, as well as the ?ndings, thoughts and lessons learned in conducting several large-scale real-world data mining bu- ness applications. The aim and objective of domain driven data mining is to study effective and ef?cient methodologies, techniques, tools, and applications that can discover and deliver actionable knowledge that can be passed on to business people for direct decision-making and action-takin

Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from data-centered pattern mining to domain driven actionable knowledge discovery for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business."

The application of formal methods to security protocol analysis has attracted increasing attention in the past two decades, and recently has been sh- ing signs of new maturity and consolidation. The development of these formal methodsismotivatedbythehostilenatureofsomeaspectsofthenetworkand the persistent e?orts of intruders, and has been widely discussed among - searchers in this ?eld. Contributions to the investigation of novel and e?cient ideas and techniques have been made through some important conferences and journals, such asESORICS, CSFW andACM Transactions in Computer Systems. Thus, formal methods have played an important role in a variety of applications such as discrete system analysis for cryptographic protocols, - lief logics and state exploration tools. A complicated security protocol can be abstractedasamanipulationofsymbolsandstructurescomposedbysymbols. The analysis of e-commerce (electronic commerce) protocols is a particular case of such symbol systems. There have been considerable e?orts in developing a number of tools for ensuring the security of protocols, both specialized and general-purpose, such as belief logic and process algebras. The application of formal methods starts with the analysis of key-distribution protocols for communication between two principals at an early stage. With the performance of transactions - coming more and more dependent on computer networks, and cryptography becoming more widely deployed, the type of application becomes more varied and complicated. The emerging complex network-based transactions such as ?nancial transactionsand secure groupcommunication have not only brought innovationstothecurrentbusinesspractice, butthey alsoposeabigchallenge to protect the information transmitted over the open network from malicious attack

This book constitutes the refereed proceedings of the Second International Workshop on Autonomous Intelligent Systems: Agents and Data Mining, AIS-ADM 2007, held in St. Petersburg, Russia in June 2007.

The 17 revised full papers and six revised short papers presented together with four invited lectures cover agent and data mining, agent competition and data mining, as well as text mining, semantic Web, and agents.

Many organizations have an urgent need of mining their multiple databases inherently distributed in branches (distributed data). In particular, as the Web is rapidly becoming an information flood, individuals and organizations can take into account low-cost information and knowledge on the Internet when making decisions. How to efficiently identify quality knowledge from different data sources has become a significant challenge. This challenge has attracted a great many researchers including the au thors who have developed a local pattern analysis, a new strategy for dis covering some kinds of potentially useful patterns that cannot be mined in traditional multi-database mining techniques. Local pattern analysis deliv ers high-performance pattern discovery from multiple databases. There has been considerable progress made on multi-database mining in such areas as hierarchical meta-learning, collective mining, database classification, and pe culiarity discovery. While these techniques continue to be future topics of interest concerning multi-database mining, this book focuses on these inter esting issues under the framework of local pattern analysis. The book is intended for researchers and students in data mining, dis tributed data analysis, machine learning, and anyone else who is interested in multi-database mining. It is also appropriate for use as a text supplement for broader courses that might also involve knowledge discovery in databases and data mining."

The Pacific Rim International Conference on Artificial Intelligence (PRICAI) is a biennial international event which focuses on Artificial Intelligence (AI) theories and technologies, and their applications which are of social and economic importance for countries in the Pacific Rim region. Seven earlier conferences were held in: Nagoya, Japan (1990); Seoul, Korea (1992); Beijing, China (1994); Cairns, Australia (1996); Singapore (1998); Melbourne, Australia (2000); and Tokyo, Japan (2002). PRICAI 2004 was the eigth in the series and was held in Auckland, New Zealand in August 2004. PRICAI 2004 had attracted a historical record number of submissions, a total of 356 papers. After careful reviews by at least two international Program Committee members or referees, 94 papers were accepted as full papers (27%) and 54 papers (15%) were accepted as posters. Authors of accepted papers came from 27 countries. This volume of the proceedings contains all the 94 full papers but only a 2-page - tended abstract of each of the accepted posters. The full papers were categorized into four sections, namely: AI foundations, computational intelligence, AI technologies and systems, and AI specific application areas. Among the papers submitted, we found "Agent Technology" to be the area having the most papers submitted. This was followed by "Evolutionary Computing", "Computational Learning", and "Image Processing".

ThePaci?c-AsiaConferenceonKnowledgeDiscoveryandDataMining(PAKDD) has been held every year since 1997. This year, the eighth in the series (PAKDD 2004) was held at Carlton Crest Hotel, Sydney, Australia, 26-28 May 2004. PAKDD is a leading international conference in the area of data mining. It p- vides an international forum for researchers and industry practitioners to share their new ideas, original research results and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition and automatic scienti?c discovery, data visualization, causal induction, and knowledge-based systems. The selection process this year was extremely competitive. We received 238 researchpapersfrom23countries, whichisthehighestinthehistoryofPAKDD, and re?ects the recognition of and interest in this conference. Each submitted research paper was reviewed by three members of the program committee. F- lowing this independent review, there were discussions among the reviewers, and when necessary, additional reviews from other experts were requested. A total of 50 papers were selected as full papers (21%), and another 31 were selected as short papers (13%), yielding a combined acceptance rate of approximately 34%. The conference accommodated both research papers presenting original - vestigation results and industrial papers reporting real data mining applications andsystemdevelopmentexperience.Theconferencealsoincludedthreetutorials on key technologies of knowledge discovery and data mining, and one workshop focusing on speci?c new challenges and emerging issues of knowledge discovery anddatamining.ThePAKDD2004programwasfurtherenhancedwithkeynote speeches by two outstanding researchers in the area of knowledge discovery and data mining: Philip Yu, Manager of Software Tools and Techniques, IBM T.J

Solving complex problems in real-world contexts, such as financial investment planning or mining large data collections, involves many different sub-tasks, each of which requires different techniques. To deal with such problems, a great diversity of intelligent techniques are available, including traditional techniques like expert systems approaches and soft computing techniques like fuzzy logic, neural networks, or genetic algorithms. These techniques are complementary approaches to intelligent information processing rather than competing ones, and thus better results in problem solving are achieved when these techniques are combined in hybrid intelligent systems. Multi-Agent Systems are ideally suited to model the manifold interactions among the many different components of hybrid intelligent systems.

This book introduces agent-based hybrid intelligent systems and presents a framework and methodology allowing for the development of such systems for real-world applications. The authors focus on applications in financial investment planning and data mining.

Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention.The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.

Intelligent agents will be the necessity of the coming century. Software agents will pilot us through the vast sea of information, by communicating with other agents. A group of cooperating agents may accomplish a task which cannot be done by any subset of them. This volume consists of selected papers from PRIMA'99, the second Paci c Rim InternationalWorkshop on Multi-Agents, held in Kyoto, Japan, on Dec- ber 2-3, 1999. PRIMA constitutes a series of workshops on autonomous agents and mul- agent systems, integrating the activities in Asia and the Pacic rim countries, such as MACC (Multiagent Systems and Cooperative Computation) in Japan, and the Australian Workshop on Distributed Arti cial Intelligence. The r st workshop, PRIMA'98, was held in conjunction with PRICAI'98, in Singapore. The aim of this workshop is to encourage activities in this e ld, and to bring togetherresearchersfromAsiaandPacic rimworkingonagentsandmultiagent issues. Unlike usual conferences, this workshop mainly discusses and explores scienti c and practical problems as raised by the participants. Participation is thus limited to professionals who have made a signi cant contribution to the topics of the workshop. Topics of interest include, but are not limited to: - multi-agent systems and their applications - agent architecture and its applications - languages for describing (multi-)agent systems - standard (multi-)agent problems - challenging research issues in (multi-)agent systems - communication and dialogues - multi-agent learning - other issues on (multi-)agent systems We received 43 submissions to this workshop from more than 10 countries.

This volume contains revisedversions of selected papers presented at the Fourth Australian Workshop on Distributed Arti?cial Intelligence (DAK 91), together with a set of invited papers. Each paper has been reviewed by at least two program committee members. The workshop was held in Brisbane, Queensland, Australia on July 17,1992. The goalof the workshopwas to promoteresearchin distributed arti?cial intelligence and multi-agent systems, both nationally and internationally. Thepapers covera widerangeofissuesin the?eldof distributed arti?cial intelligence and multi-agent systems, such as theories, languages, and applications. Manypeoplecontributedtothesuccessofthis workshop. Wewouldliketothank all the authors who submitted papers to the workshop. Many thanks also to the members of the programme committee who diligently reviewed all the papers submitted. Finally, we thank the editorial sta? of Springer-Verlag for publishing this contribution to the Lecture Notes in Arti?cial Intelligence series. October 1998 Chengqi Zhang and Dickson Lukose Programme Committee Chengqi Zhang (Co-Chair) University of New England, Australia Dickson Lukose (Co-Chair) Brightware Inc., USA Victor Lesser University of Massachusetts, USA Je?rey S. Rosenschein Hebrew University, Israel Toshiharu Sugawara NTT Basic Research Labs, Japan Rose Dieng ACACIA Project, INRIA, France Norbert Glaser CRIN-INRIA Lorraine, France Sascha Ossowski Rey Juan Carlos Univ, Spain Michael Luck University of Warwick, UK Mark d Inverno University of Westminster, UK Tim Norman University of London, UK Douglas Norrie University of Calgary, Canada Bernard Moulin Laval University, Canada Zhisheng Huang University of London, UK Minjie Zhang Edith Cowan University, Australia Brahim Chaib-draa Laval University, Canada Table of Contents TeamFormationbySelf-InterestedMobileAgents . . . . . . . . . . . . . . . . . . . . . . . . . ."

This book constitutes the thoroughly refereed post-workshop proceedings of three workshops held in conjunction with the 10th Australian Joint Conference on Artificial Intelligence in Perth, Australia, in December 1997.
The 17 revised full papers presented were carefully reviewed for inclusion in the book. The book is divided into sections on formal methods of agency, reasoning agents, communication and coordination, social interaction, and practical issues for distributed artificial intelligence systems.

This book constitutes the strictly refereed post-workshop proceedings originating from the Second Australian Workshop on Distributed Artificial Intelligence, held in Cairns, QLD, Australia, in August 1996, as a satellite meeting of PRICAI'96.
The 13 revised full papers presented have been selected for inclusion in the book during a very careful and iterated process of reviewing and improvement. Among these papers are three invited ones, by leading scientists, solicited in order to round off the overall presentation and coverage of relevant topics. A wide range of multi-agent systems issues is covered including methodologies, cooperation, conflict resolution, applications, mobility, adaptation, negotiation, and implementations.

This book constitutes the refereed proceedings of the First Australian Workshop on Distributed Artificial Intelligence, held in Canberra, ACT, Australia, in November 1995.
The 16 full papers included in the book are revised versions of the contributions accepted for presentation at the workshop; they cover a wide range of current issues in the field of DAI, such as formal frameworks, methodology, theory, and architecture; various aspects of agents and multi-agent systems are addressed.

This book constitutes the refereed proceedings of the Second International Conference on Data Science, ICDS 2015, held in Sydney, Australia, during August 8-9, 2015. The 19 revised full papers and 5 short papers presented were carefully reviewed and selected from 31 submissions. The papers focus on the following topics: mathematical issues in data science; big data issues and applications; data quality and data preparation; data-driven scientific research; evaluation and measurement in data service; big data mining and knowledge management; case study of data science; social impacts of data science.

There is often a large number of association rules discovered in data mining practice, making it difficult for users to identify those that are of particular interest to them. Therefore, it is important to remove insignificant rules and prune redundancy as well as summarize, visualize, and post-mine the discovered rules. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction provides a systematic collection on post-mining, summarization and presentation of association rules, and new forms of association rules. This book presents researchers, practitioners, and academicians with tools to extract useful and actionable knowledge after discovering a large number of association rules.