This book constitutes the refereed proceedings of the 6th International Workshop on Agents and Data Mining Interaction, ADMI 2010, held in Toronto, Canada, in May 2010. The 15 revised full papers presented were carefully reviewed and selected from 37 submissions. The papers are organized in topical sections on agents for data mining; data mining for agents; data mining in agents; and agent mining applications.

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.

Data Mining and Multi agent Integration aims to re?ect state of the art research and development of agent mining interaction and integration (for short, agent min ing). The book was motivated by increasing interest and work in the agents data min ing, and vice versa. The interaction and integration comes about from the intrinsic challenges faced by agent technology and data mining respectively; for instance, multi agent systems face the problem of enhancing agent learning capability, and avoiding the uncertainty of self organization and intelligence emergence. Data min ing, if integrated into agent systems, can greatly enhance the learning skills of agents, and assist agents with predication of future states, thus initiating follow up action or intervention. The data mining community is now struggling with mining distributed, interactive and heterogeneous data sources. Agents can be used to man age such data sources for data access, monitoring, integration, and pattern merging from the infrastructure, gateway, message passing and pattern delivery perspectives. These two examples illustrate the potential of agent mining in handling challenges in respective communities. There is an excellent opportunity to create innovative, dual agent mining interac tion and integration technology, tools and systems which will deliver results in one new technology.

This book offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. It bridges the gap between business expectations and research output.

Multi-agent system (MAS) is an expanding field in science and engineering. It merges classical fields like game theory with modern ones like machine learning and computer science. This book provides a succinct introduction to the subject, covering the theoretical fundamentals as well as the latter developments in a coherent and clear manner. The book is centred on practical applications rather than introductory topics. Although it occasionally makes reference to the concepts involved, it will do so primarily to clarify real-world applications. The inner chapters cover a wide spectrum of issues related to MAS uses, which include collision avoidance, automotive applications, evacuation simulation, emergence analyses, cooperative control, context awareness, data (image) mining, resilience enhancement and the management of a single-user multi-robot.

Scientific Essay from the year 2014 in the subject Computer Science - Applied, grade: A, University of North Texas (Department of Computer Science), course: Distributed and Parallel Databases, language: English, abstract: With the increase in the usage of databases in various fields and domains, to overcome the challenges in a centralized data mining environment, more and more databases are distributed in networks. The objective of distributed data mining is to perform data mining operations based on the type and availability of distributed resources. To make a proper choice of a particular DDM system/model, the basic differences between each of them must be understood. This paper produces a survey of some of the DDM systems available. It mainly focusses on the homogeneous DDM models. It discusses methods based on semantic web and grid, multi-agent, mobile agent and i-Analyst. A hybrid method AGrIP is also discussed. A comparative analysis is made considering different key issues of DDM. Each method is described in detail by its method/algorithm.

"This collection offers tools, designs, and outcomes of the utilization of data mining and warehousing technologies, such as algorithms, concept lattices, multidimensional data, and online analytical processing. With more than 300 chapters contributed by over 575 experts from around the globe, this authoritative collection will provide libraries with the essential reference on data mining and warehousing"--Provided by publisher.

The Internet has become a major communication medium, where virtually any kind of content can be transferred instantly and reliably between individual users and entire organizations located in any part of the globe. Consequently, more effective and efficient methods and technologies are needed to make the most of the Web's nearly unlimited potential. The new Web-related research directions include intelligent methods usually associated with the fields of computational intelligence, soft computing, and data mining. This book presents state-of-the-art developments in the area of computationally intelligent methods applied to various aspects and ways of Web exploration and Web mining. Some novel data mining algorithms that can lead to more effective and intelligent Web-based systems are also described. Scientists, engineers, and research students are expected to find many inspiring ideas in this volume.

A magazine for designers of interactive products.