LNAI 3191 Multi agent Systems and Distributed
Data Mining 1st Edition by Chris Giannella,
Ruchita Bhargava, Hillol Kargupta ISBN
9783540206460 354020646X pdf download
https://ebookball.com/product/lnai-3191-multi-agent-systems-and-
distributed-data-mining-1st-edition-by-chris-giannella-ruchita-
bhargava-hillol-kargupta-isbn-9783540206460-354020646x-11934/
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LNAI 3171 An Efficient Clustering Method for High Dimensional Data
Mining 1st Edition by Jae Woo Chang, Yong Ki Kim ISBN 9783540206460
354020646X
https://ebookball.com/product/lnai-3171-an-efficient-clustering-
method-for-high-dimensional-data-mining-1st-edition-by-jae-woo-
chang-yong-ki-kim-isbn-9783540206460-354020646x-8848/
LNAI 2903 Robustness for Evaluating Rule Generalization Capability in
Data Mining 1st Edition by Dianhui Wang, Tharam Dillon, Xiaohang Ma
ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-robustness-for-
evaluating-rule-generalization-capability-in-data-mining-1st-
edition-by-dianhui-wang-tharam-dillon-xiaohang-ma-
isbn-9783540206460-354020646x-11134/
LNAI 3127 Concept Based Data Mining with Scaled Labeled Graphs 1st
Edition by Bernhard Ganter, Peter Grigoriev, Sergei Kuznetsov, Mikhail
Samokhin ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-3127-concept-based-data-
mining-with-scaled-labeled-graphs-1st-edition-by-bernhard-ganter-
peter-grigoriev-sergei-kuznetsov-mikhail-samokhin-
isbn-9783540206460-354020646x-13116/
LNAI 2926 Intentional Analysis for Distributed Knowledge Management
1st Edition by Anna Perini, Paolo Bresciani, Eric Yu, Alessandra
Molani ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2926-intentional-analysis-for-
distributed-knowledge-management-1st-edition-by-anna-perini-
paolo-bresciani-eric-yu-alessandra-molani-
isbn-9783540206460-354020646x-13680/
,LNAI 2903 Pareto Neuro Ensembles 1st Edition by Hussein Abbass ISBN
9783540206460 354020646X
https://ebookball.com/product/lnai-2903-pareto-neuro-
ensembles-1st-edition-by-hussein-abbass-
isbn-9783540206460-354020646x-9200/
LNAI 2903 Towards Automated Creation of Image Interpretation Systems
1st Edition by Ilya Levner, Vadim Bulitko, Lihong Li, Greg Lee,
Russell Greiner ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-towards-automated-
creation-of-image-interpretation-systems-1st-edition-by-ilya-
levner-vadim-bulitko-lihong-li-greg-lee-russell-greiner-
isbn-9783540206460-354020646x-9084/
LNAI 2903 MML Classification of Music Genres 1st Edition by Adrian
Bickerstaffe, Enes Makalic ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-mml-classification-of-
music-genres-1st-edition-by-adrian-bickerstaffe-enes-makalic-
isbn-9783540206460-354020646x-14280/
LNAI 2903 Model Based Reinforcement Learning for Alternating Markov
Games 1st Edition by Drew Mellor ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-model-based-
reinforcement-learning-for-alternating-markov-games-1st-edition-
by-drew-mellor-isbn-9783540206460-354020646x-10942/
Agent Intelligence Through Data Mining Multiagent Systems Artificial
Societies and Simulated Organizations 14 1st edition by Andreas
Symeonidis, Pericles Mitkas ISBN 0387243526 Â 978-0387243528
https://ebookball.com/product/agent-intelligence-through-data-
mining-multiagent-systems-artificial-societies-and-simulated-
organizations-14-1st-edition-by-andreas-symeonidis-pericles-
mitkas-isbn-0387243526-978-0387243528-19574/
, Multi-agent Systems and Distributed Data Mining
Chris Giannella, Ruchita Bhargava, and Hillol Kargupta
Department of Computer Science and Electrical Engineering
University of Maryland Baltimore County
Baltimore, MD 21250 USA
{cgiannel,ruchita1,hillol}@cs.umbc.edu
Abstract. Multi-agent systems offer an architecture for distributed problem solv-
ing. Distributed data mining algorithms specialize on one class of such distributed
problem solving tasks—analysis and modeling of distributed data. This paper of-
fers a perspective on distributed data mining algorithms in the context of multi-
agents systems. It particularly focuses on distributed clustering algorithms and
their potential applications in multi-agent-based problem solving scenarios. It dis-
cusses potential applications in the sensor network domain, reviews some of the
existing techniques, and identifies future possibilities in combining multi-agent
systems with the distributed data mining technology.
Keywords: multi-agent systems, distributed data mining, clustering
1 Introduction
Multi-agent systems (MAS) often deal with complex applications that require distributed
problem solving. In many applications the individual and collective behavior of the
agents depend on the observed data from distributed sources. In a typical distributed
environment analyzing distributed data is a non-trivial problem because of many con-
straints such as limited bandwidth (e.g. wireless networks), privacy-sensitive data, dis-
tributed compute nodes, only to mention a few. The field of Distributed Data Mining
(DDM) deals with these challenges in analyzing distributed data and offers many al-
gorithmic solutions to perform different data analysis and mining operations in a fun-
damentally distributed manner that pays careful attention to the resource constraints.
Since multi-agent systems are also distributed systems, combining DDM with MAS for
data intensive applications is appealing.
This paper makes an effort to underscore the possible synergy between multi-agent
systems and distributed data mining technology. It particularly focuses on distributed
clustering, a problem finding increasing number of applications in sensor networks,
distributed information retrieval, and many other domains. The paper discusses one of
these application domains, illustrates the ideas, and reviews existing work in this area.
Although, the power of DDM is not just restricted to clustering, this paper chooses to
restrict the scope for the sake of brevity.
The paper is organized as follows. Section 2 provides the motivation behind the
material presented in this paper. Section 3 introduces DDM and presents an overview
of the field. Section 4 focuses on a particular portion of the DDM literature and takes
M. Klusch et al. (Eds.): CIA 2004, LNAI 3191, pp. 1–15, 2004.
c Springer-Verlag Berlin Heidelberg 2004
Data Mining 1st Edition by Chris Giannella,
Ruchita Bhargava, Hillol Kargupta ISBN
9783540206460 354020646X pdf download
https://ebookball.com/product/lnai-3191-multi-agent-systems-and-
distributed-data-mining-1st-edition-by-chris-giannella-ruchita-
bhargava-hillol-kargupta-isbn-9783540206460-354020646x-11934/
Explore and download more ebooks or textbooks
at ebookball.com
, Get Your Digital Files Instantly: PDF, ePub, MOBI and More
Quick Digital Downloads: PDF, ePub, MOBI and Other Formats
LNAI 3171 An Efficient Clustering Method for High Dimensional Data
Mining 1st Edition by Jae Woo Chang, Yong Ki Kim ISBN 9783540206460
354020646X
https://ebookball.com/product/lnai-3171-an-efficient-clustering-
method-for-high-dimensional-data-mining-1st-edition-by-jae-woo-
chang-yong-ki-kim-isbn-9783540206460-354020646x-8848/
LNAI 2903 Robustness for Evaluating Rule Generalization Capability in
Data Mining 1st Edition by Dianhui Wang, Tharam Dillon, Xiaohang Ma
ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-robustness-for-
evaluating-rule-generalization-capability-in-data-mining-1st-
edition-by-dianhui-wang-tharam-dillon-xiaohang-ma-
isbn-9783540206460-354020646x-11134/
LNAI 3127 Concept Based Data Mining with Scaled Labeled Graphs 1st
Edition by Bernhard Ganter, Peter Grigoriev, Sergei Kuznetsov, Mikhail
Samokhin ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-3127-concept-based-data-
mining-with-scaled-labeled-graphs-1st-edition-by-bernhard-ganter-
peter-grigoriev-sergei-kuznetsov-mikhail-samokhin-
isbn-9783540206460-354020646x-13116/
LNAI 2926 Intentional Analysis for Distributed Knowledge Management
1st Edition by Anna Perini, Paolo Bresciani, Eric Yu, Alessandra
Molani ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2926-intentional-analysis-for-
distributed-knowledge-management-1st-edition-by-anna-perini-
paolo-bresciani-eric-yu-alessandra-molani-
isbn-9783540206460-354020646x-13680/
,LNAI 2903 Pareto Neuro Ensembles 1st Edition by Hussein Abbass ISBN
9783540206460 354020646X
https://ebookball.com/product/lnai-2903-pareto-neuro-
ensembles-1st-edition-by-hussein-abbass-
isbn-9783540206460-354020646x-9200/
LNAI 2903 Towards Automated Creation of Image Interpretation Systems
1st Edition by Ilya Levner, Vadim Bulitko, Lihong Li, Greg Lee,
Russell Greiner ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-towards-automated-
creation-of-image-interpretation-systems-1st-edition-by-ilya-
levner-vadim-bulitko-lihong-li-greg-lee-russell-greiner-
isbn-9783540206460-354020646x-9084/
LNAI 2903 MML Classification of Music Genres 1st Edition by Adrian
Bickerstaffe, Enes Makalic ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-mml-classification-of-
music-genres-1st-edition-by-adrian-bickerstaffe-enes-makalic-
isbn-9783540206460-354020646x-14280/
LNAI 2903 Model Based Reinforcement Learning for Alternating Markov
Games 1st Edition by Drew Mellor ISBN 9783540206460 354020646X
https://ebookball.com/product/lnai-2903-model-based-
reinforcement-learning-for-alternating-markov-games-1st-edition-
by-drew-mellor-isbn-9783540206460-354020646x-10942/
Agent Intelligence Through Data Mining Multiagent Systems Artificial
Societies and Simulated Organizations 14 1st edition by Andreas
Symeonidis, Pericles Mitkas ISBN 0387243526 Â 978-0387243528
https://ebookball.com/product/agent-intelligence-through-data-
mining-multiagent-systems-artificial-societies-and-simulated-
organizations-14-1st-edition-by-andreas-symeonidis-pericles-
mitkas-isbn-0387243526-978-0387243528-19574/
, Multi-agent Systems and Distributed Data Mining
Chris Giannella, Ruchita Bhargava, and Hillol Kargupta
Department of Computer Science and Electrical Engineering
University of Maryland Baltimore County
Baltimore, MD 21250 USA
{cgiannel,ruchita1,hillol}@cs.umbc.edu
Abstract. Multi-agent systems offer an architecture for distributed problem solv-
ing. Distributed data mining algorithms specialize on one class of such distributed
problem solving tasks—analysis and modeling of distributed data. This paper of-
fers a perspective on distributed data mining algorithms in the context of multi-
agents systems. It particularly focuses on distributed clustering algorithms and
their potential applications in multi-agent-based problem solving scenarios. It dis-
cusses potential applications in the sensor network domain, reviews some of the
existing techniques, and identifies future possibilities in combining multi-agent
systems with the distributed data mining technology.
Keywords: multi-agent systems, distributed data mining, clustering
1 Introduction
Multi-agent systems (MAS) often deal with complex applications that require distributed
problem solving. In many applications the individual and collective behavior of the
agents depend on the observed data from distributed sources. In a typical distributed
environment analyzing distributed data is a non-trivial problem because of many con-
straints such as limited bandwidth (e.g. wireless networks), privacy-sensitive data, dis-
tributed compute nodes, only to mention a few. The field of Distributed Data Mining
(DDM) deals with these challenges in analyzing distributed data and offers many al-
gorithmic solutions to perform different data analysis and mining operations in a fun-
damentally distributed manner that pays careful attention to the resource constraints.
Since multi-agent systems are also distributed systems, combining DDM with MAS for
data intensive applications is appealing.
This paper makes an effort to underscore the possible synergy between multi-agent
systems and distributed data mining technology. It particularly focuses on distributed
clustering, a problem finding increasing number of applications in sensor networks,
distributed information retrieval, and many other domains. The paper discusses one of
these application domains, illustrates the ideas, and reviews existing work in this area.
Although, the power of DDM is not just restricted to clustering, this paper chooses to
restrict the scope for the sake of brevity.
The paper is organized as follows. Section 2 provides the motivation behind the
material presented in this paper. Section 3 introduces DDM and presents an overview
of the field. Section 4 focuses on a particular portion of the DDM literature and takes
M. Klusch et al. (Eds.): CIA 2004, LNAI 3191, pp. 1–15, 2004.
c Springer-Verlag Berlin Heidelberg 2004
