Nothing Special   »   [go: up one dir, main page]

Skip to main content

Multi-agent Systems for Distributed Data Mining Techniques: An Overview

  • Chapter
  • First Online:
Big Data Intelligence for Smart Applications

Abstract

The term “multi-agent systems” (MAS) refers to a mechanism that is used to create goal-oriented autonomous agents in a shared environment and have communication and coordination capabilities. This goal-oriented mechanism supports distributed data mining (DM) to implement various techniques for distributed clustering, classification, and prediction. Different distributed DM (DDM) techniques, MASs, the advantages of MAS-based DDM, and various MAS-based DDM approaches proposed by researchers are reviewed in this study.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • A.A. Ali, P. Vařacha, S. Krayem, P. Žáček, A. Urbanek, Distributed data mining systems: techniques, approaches and algorithms, in MATEC Web of Conferences, vol. 210. (EDP Sciences, 2018), p. 04038

    Google Scholar 

  • A. Amir, B. Srinivasan, A.I. Khan, Distributed classification for image spam detection. Multimedia Tools Appl. 77(11), 13249–13278 (2018)

    Article  Google Scholar 

  • M.A. Almaiah, A. Al-Khasawneh, Investigating the main determinants of mobile cloud computing adoption in university campus. Educ. Inf. Technol. 25(4), 3087–3107 (2020)

    Article  Google Scholar 

  • M. Adil, R. Khan, M.A. Almaiah, M. Al-Zahrani, M. Zakarya, M.S. Amjad, R. Ahmed, MAC-AODV based mutual authentication scheme for constraint oriented networks. IEEE Access 4(8), 44459–44469 (2020)

    Article  Google Scholar 

  • M. Adil, R. Khan, M.A. Almaiah, M. Binsawad, J. Ali, A. Al Saaidah, Q.T.H. Ta, An efficient load balancing scheme of energy gauge nodes to maximize the lifespan of constraint oriented networks. IEEE Access 8, 148510–148527 (2020)

    Article  Google Scholar 

  • M. Adil, M.A. Almaiah, A. Omar Alsayed, O. Almomani, An anonymous channel categorization scheme of edge nodes to detect jamming attacks in wireless sensor networks. Sensors 20(8), 2311 (2020)

    Article  Google Scholar 

  • A.K. Al Hwaitat, M.A. Almaiah, O. Almomani, M. Al-Zahrani, R.M. Al-Sayed, R.M. Asaifi, K.K. Adhim, A. Althunibat, A. Alsaaidah, Improved security particle swarm optimization (PSO) algorithm to detect radio jamming attacks in mobile networks. Quintana 11(4), 614–624 (2020)

    Google Scholar 

  • M. Adil, R. Khan, J. Ali, B.H. Roh, Q.T. Ta, M.A. Almaiah, An energy proficient load balancing routing scheme for wireless sensor networks to maximize their lifespan in an operational environment. IEEE Access 31(8), 163209–163224 (2020)

    Article  Google Scholar 

  • M.A. Almaiah, Z. Dawahdeh, O. Almomani, A. Alsaaidah, A. Al-khasawneh, S. Khawatreh, A new hybrid text encryption approach over mobile ad hoc network. Int. J. Electr. Comput. Eng. (IJECE) 10(6), 6461–6471 (2020)

    Article  Google Scholar 

  • M.A. Almaiah, A. Al-Zahrani, O. Almomani, A.K. Alhwaitat, Classification of cyber security threats on mobile devices and applications. Artif. Intell. Blockchain Future Cybersecur. Appl. 107

    Google Scholar 

  • M.A. Almaiah, A new scheme for detecting malicious attacks in wireless sensor networks based on blockchain technology. Artif. Intell. Blockchain Future Cybersecur. Appl. 217

    Google Scholar 

  • M.A. Almaiah, M. Al-Zahrani, Multilayer neural network based on MIMO and channel estimation for impulsive noise environment in mobile wireless networks. Int. J. Adv. Trends Comput. Sci. Eng. 9(1), 315–321 (2020)

    Article  Google Scholar 

  • M.A. Almaiah, M.M. Alamri, Proposing a new technical quality requirements for mobile learning applications. J. Theore. Appl. Inf. Technol 96(19) (2018)

    Google Scholar 

  • S. Bandyopadhyay, C. Giannella, U. Maulik, H. Kargupta, K. Liu, S. Datta, Clustering distributed data streams in peer-to-peer environments. Inf. Sci. 176(14), 1952–1985 (2006)

    Article  Google Scholar 

  • N. Bouchemal, N. Bouchemal, Intelligent ERP based multi agent systems and cloud computing. In International Conference on Machine Learning for Networking (Springer, Cham, Nov. 2018), pp. 378–386

    Google Scholar 

  • D. Chiang, C. Lin, M. Chen, The adaptive approach for storage assignment by mining data of warehouse management system for distribution centres. Enterp. Inf. Syst. 5(2), 219–234 (2011)

    Article  Google Scholar 

  • C.Y. Chen, J.J. Huang, Double deep autoencoder for heterogeneous distributed clustering. Information 10(4), 144 (2019)

    Article  Google Scholar 

  • C. Clifton, M. Kantarcioglou, X. Lin, M. Zhu, Tools for privacy preserving distributed data mining. ACM SIGKDD Exp. 4(2) (2002)

    Google Scholar 

  • A. Cuzzocrea, Models and algorithms for high-performance distributed data mining. Elsevier J. Parallel Distrib. Comput. 73(93), 281–283 (2013)

    Article  Google Scholar 

  • R. Claes, T. Holvoet, D. Weyns, A decentralized approach for anticipatory vehicle routing using delegate multiagent systems. IEEE Trans. Intell. Transp. Syst. 12(2), 364–373 (2011)

    Article  Google Scholar 

  • P. Charlton, R. Cattoni, Evaluating the deployment of FIPA standards when developing application services. Int. J. Pattern Recogn. Artif. Intell. 15(03) (2001)

    Google Scholar 

  • P. Charlton, R. Cattoni, A. Potrich, E. Mamdani, Evaluating the FIPA standards and their role in achieving cooperation. In Multi-Agent Systems (IEEE Xplore, Aug. 2002)

    Google Scholar 

  • A. Dorri, S.S. Kanhere, R. Jurdak, Multi-agent systems: A survey. IEEE. Access 6, 28573–28593 (2018)

    Article  Google Scholar 

  • G. Dudek, M.R. Jenkin, E. Milios, D. Wilkes, A taxonomy for multi-agent robotics. Auton. Robot. 3(4), 375–397 (1996)

    Article  Google Scholar 

  • Y. Duan, B.X. Cui, X.H. Xu, A multi-agent reinforcement learning approach to robot soccer. Artif. Intell. Rev. 38(3), 193–211 (2012)

    Article  Google Scholar 

  • R. Domínguez, S. Cannella, J.M. Framinan, Scope: a multi-agent system tool for supply chain network analysis, in EUROCON 2015-International Conference on Computer as a Tool (EUROCON), IEEE (IEEE, 2015), pp. 1–5

    Google Scholar 

  • H. Du, S. Li, S. Ding, Bounded consensus algorithms for multiagent systems in directed networks. Asian J. Control 15(1), 282–291 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  • FIPA, FIPA Abstract Architecture Specification, SC 00001L (2002a). http://www.fipa.org/specs/fipa00001/SC00001l.pdf

  • FIPA, SC00067F (2002b). http://www.fipa.org/specs/fipa00067/SC00067F.pdf

  • I.E. Foukarakis, A.I. Kostaridis, C.G. Biniaris, D.I. Kaklamani, I.S. Venieris, Webmages: An Agent Platform Based on Web Services

    Google Scholar 

  • W. Gan, J.C.W. Lin, H.C. Chao, J. Zhan, Data mining in distributed environment: a survey. Wiley Interdiscip. Rev.: Data Mining Knowl. Dis. 7(6), e1216 (2017)

    Google Scholar 

  • A.P. Garcia, J. Oliver, D. Gosch, An intelligent agent-based distributed architecture for smart-grid integrated network management, in 2010 IEEE 35th Conference on Local Computer Networks (LCN) (IEEE, 2010), pp. 1013–1018

    Google Scholar 

  • A. González-Briones, F. De La Prieta, M. Mohamad, S. Omatu, J. Corchado, Multi-agent systems applications in energy optimization problems: a state-of-the-art review. Energies 11(8), 1928 (2018)

    Article  Google Scholar 

  • M. Gatti, P. Cavalin, S.B. Neto, C. Pinhanez, C. dos Santos, D. Gribel, A.P. Appel, Large-scale multi-agent-based modeling and simulation of microblogging-based online social network, in International Workshop on Multi-Agent Systems and Agent-Based Simulation (Springer, 2013), pp. 17–33

    Google Scholar 

  • A. Goryashchenko, Algorithm and application development for the agents group formation in a multi-agent system using SPADE system, in Future of Information and Communication Conference. (Springer, Cham, Mar. 2019), pp. 1136–1143

    Google Scholar 

  • A. Hudaib, M.H. Qasem, N. Obeid, FIPA-Based semi-centralized protocol for negotiation, in Proceedings of the Computational Methods in Systems and Software (Springer, Cham, Sept. 2017), pp. 135–149

    Google Scholar 

  • D. Helbing, Agent-based modeling, in Social self-organization (Springer, 2012), pp. 25–70

    Google Scholar 

  • C. Iddianozie, G. McArdle, A transfer learning paradigm for spatial networks, in Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (ACM, Apr. 2019), pp. 659–666

    Google Scholar 

  • I.F. Ilyas, X. Chu, X. Trends in cleaning relational data: Consistency and deduplication. Found. Trends® Databases 5(4), 281–393 (2015)

    Google Scholar 

  • T. Ishida, H. Yukoi, Y. Kakazu, Self-organized norms of behaviour under interactions of selfish agents, in IEEE SMC ’99 Conference Proceedings and IEEE Xplore Systems, Man and Cybernetics, 1999, Aug. 2002

    Google Scholar 

  • E. Januzaj, H.P. Kriegel, M, Pfeifle, Dbdc: density based distributed clustering, in International Conference on Extending Database Technology. (Springer, Berlin, Heidelberg, Mar. 2004), pp. 88–105

    Google Scholar 

  • S. Jeong, U. Choi, J. Ahn, Distributed clustering algorithm for UAV systems, in AIAA Scitech 2019 Forum, p. 1795 (2019)

    Google Scholar 

  • L.C. Jain, D. Srinivasan, Innovations in Multi-agent Systems and Applications (Springer, 2010)

    Google Scholar 

  • F. Januário, A. Cardoso, P. Gil, Multi-Agent framework for resilience enhancement over a WSAN, in 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (IEEE, July 2018), pp. 110–113

    Google Scholar 

  • Y. Jiang, J. Jiang, Understanding social networks from a multiagent perspective. IEEE Trans. Parallel Distrib. Syst. 25(10), 2743–2759 (2014)

    Article  Google Scholar 

  • N.R. Jennings, K. Sycara, M. Wooldridge, A Roadmap of Agent Research and Development, Springer: Autonomous Agents and Multi-Agent Systems, vol. 1, Issue 1, pp 7–38 (1998)

    Google Scholar 

  • K. Kasemsap, Multifaceted applications of data mining, business intelligence, and knowledge management, in Intelligent Systems: Concepts, Methodologies, Tools, and Applications, (IGI Global, 2018), pp. 810–825

    Google Scholar 

  • K. Kargupt, Chan, Distributed and parallel data mining: emergence, growth and future directions, in Advances in Distributed Data Mining, ed. by H. Kargupta, P. Chan (AAAI Press, 1999), pp. 407–416

    Google Scholar 

  • M.N. Khan, H.U. Rahman, M.A. Almaiah, M.Z. Khan, A. Khan, M. Raza, M. Al-Zahrani, O. Almomani, R. Khan, Improving energy efficiency with content-based adaptive and dynamic scheduling in wireless sensor networks. IEEE Access 25(8), 176495–176520 (2020)

    Article  Google Scholar 

  • K. Kannan, K. Raja, A. Rajakumar, P.K. Nizar Banu, E-Business Decision Support System for Online Shopping using MAS with Ontology and JADE Methodology (2019)

    Google Scholar 

  • K. Kravari, E. Kontopoulos, N. Bassiliades,. EMERALD: a multi-agent system for knowledge-based reasoning interoperability in the semantic web, in Hellenic Conference on Artificial Intelligence (Springer, Berlin, Heidelberg, May 2010), pp. 173–182

    Google Scholar 

  • K. Kravari, N. Bassiliades, H. Boley, Cross-community interoperation between knowledge-based multi-agent systems: A study on EMERALD and Rule Responder. Expert Syst. Appl. 39(10), 9571–9587 (2012)

    Article  Google Scholar 

  • H. Li, L. Xu, J. Wang, Z. Mo, Feature space theory in data mining: transformations between extensions and intensions in knowledge representation. Expert Syst. 20(2), 60–71 (2003)

    Article  Google Scholar 

  • B. Liu, S. Cao, W. He, Distributed data mining for e-business. Inf. Technol. Manag. 12(2), 67–79 (2011)

    Article  Google Scholar 

  • T. Li, F. De la Prieta Pintado, J.M. Corchado, J. Bajo, Multi-source homogeneous data clustering for multi-target detection from cluttered background with misdetection. Appl. Soft Comput. 60, 436–446 (2017)

    Article  Google Scholar 

  • R. Lu, K. Heung, A.H. Lashkari, A.A. Ghorbani, A lightweight privacy-preserving data aggregation scheme for fog computing-enhanced IoT. IEEE Access 5, 3302–3312 (2017)

    Article  Google Scholar 

  • O. Lopez Ortega, F. Castro Espinoza, O. Perez-Cortes, An Intelligent Multiagent System to Create and Classify Fractal Music (Springer-Verlag GmbH Austria, Jan. 2018)

    Google Scholar 

  • A. Louati, S. Elkosantini, S. Darmoul, H. Louati, Multi-agent preemptive longest queue first system to manage the crossing of emergency vehicles at interrupted intersections. Eur. Transp. Res. Rev. 10(2), 52 (2018)

    Article  MATH  Google Scholar 

  • H. Li, C. Ming, S. Shen, W. Wong, Event-triggered control for multi-agent systems with randomly occurring nonlinear dynamics and time-varying delay. J. Franklin Inst. 351(5), 2582–2599 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  • C. Moemeng, V. Gorodetsky, V., Z. Zuo, Y. Yang, C. Zhang, Agent-based distributed data mining: a survey, in Data Mining and Multi-Agent Integration, (Springer, Boston, MA, 2009), pp. 47–58

    Google Scholar 

  • R. Mendes, J.P. Vilela, Privacy-preserving data mining: methods, metrics, and applications. IEEE Access 5, 10562–10582 (2017)

    Article  Google Scholar 

  • P. Montero-Manso, L. Morán-Fernández, V. Bolón-Canedo, J.A. Vilar, A. Alonso-Betanzos, Distributed classification based on distances between probability distributions in feature space. Inf. Sci. (2018)

    Google Scholar 

  • L.S. Melo, R.F. Sampaio, R.P.S. Leão, G.C. Barroso, J.R. Bezerra, Python‐based multi‐agent platform for application on power grids. Int. Trans. Electr. Energy Syst. e12012 (2019).

    Google Scholar 

  • S.D. McArthur, E.M. Davidson, V.M. Catterson, A.L. Dimeas, N.D. Hatziargyriou, F. Ponci, T. Funabashi, Multi-agent systems for power engineering applicationsâ Tpart i: concepts, approaches, and technical challenges. IEEE Trans. Power Syst. 22(4), 1743–1752 (2007)

    Article  Google Scholar 

  • L. Ma, H. Min, S. Wang, Y. Liu, S. Liao, An overview of research in distributed attitude coordination control. IEEE/CAA J. Automatica Sinica 2(2), 121–133 (2015)

    Article  MathSciNet  Google Scholar 

  • L. Ma, Y. Zhang, Hierarchical social network analysis using multiagent systems: a school system case, in 2014 IEEE International Conference on Systems, Man and Cybernetics (SMC) (IEEE, 2014), pp. 1412–1419

    Google Scholar 

  • L. Niu, N. Feng, Research on cooperation control of chassis multi-agent, in 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, vol. 2 (IEEE, Aug. 2010), pp. 464–467

    Google Scholar 

  • M.A. Ouda, S.A. Salem, I.A. Ali, E.S.M. Saad, Privacy-preserving data mining (PPDM) method for horizontally partitioned data. Int. J. Comput. Sci. 9(5), 339–347 (2012)

    Google Scholar 

  • N. Obeid, A. Moubaiddin, A. Towards a formal model of knowledge sharing in complex systems, in Smart Information and Knowledge Management (Springer, Berlin, Heidelberg, 2010), pp. 53–82

    Google Scholar 

  • R. Olszewski, P. Pałka, A. Turek, B. Kietlińska, T. Płatkowski, M. Borkowski, Spatiotemporal modeling of the smart city residents’ activity with multi-agent systems. Appl. Sci. 9(10), 2059 (2019)

    Article  Google Scholar 

  • R. Olfati-Saber, R.M. Murray, Consensus problems in networks of agents with switching topology and time-delays. IEEE Trans. Autom. Control 49(9), 1520–1533 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  • A. Patel, W. Qi, C. Wills, A review and future research directions of secure and trustworthy mobile agent-based e-marketplace systems. Inf. Manag. Comput. Secur. 18(3), 144–161 (2010)

    Article  Google Scholar 

  • L. Penait, S. Luke, Co-operative Multi-Agent Learning: The State of the Art, Springer Science + Business Media, Netherlands: Autonomous Agents and Multi-Agent Systems, vol. 11, pp. 387–434 (2005)

    Google Scholar 

  • M.H. Qasem, N. Obeid, A. Hudaib M.A Almaiah, A. Al-Zahrani, A. Al-khasawneh, Multi-Agent System Combined with Distributed Data Mining for Mutual Collaboration Classification (IEEE Access, 20 Apr. 2021)

    Google Scholar 

  • A.M. Ranwa, F. Bilal, F., Q. Alejandro, Distributed Classification of Urban Congestion Using VANET (2019). arXiv:1904.12685.

  • Russell, A.P. Norvig, Intelligence, “A modern approach”, vol. 25 (Artificial Intelligence. Prentice-Hall, Egnlewood Cliffs, 1995), p. 27

    Google Scholar 

  • Y. Rizk, M. Awad, E.W. Tunstel, Decision making in multiagent systems: a survey. IEEE Trans. Cogn. Dev. Syst. 10(3), 514–529 (2018)

    Article  Google Scholar 

  • H. Rezaee, F. Abdollahi, Average consensus over high-order multiagent systems. IEEE Trans. Autom. Control 60(11), 3047–3052 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  • Y. Ren, D. Fan, Q. Feng, Z. Wang, B. Sun, D. Yang, Agent-based restoration approach for reliability with load balancing on smart grids. Appl. Energy 249, 46–57 (2019)

    Article  Google Scholar 

  • Z. Ruiz-Chavez, J. Salvador-Meneses, S. Díaz-Quilachamín, C. Mejía-Astudillo, (, October). Solid Waste Management using Georeferenced Multi-agent Systems. In 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM) (IEEE, Oct. 2018), pp. 1–6

    Google Scholar 

  • V. Sawant, K. Shah, A review of distributed data mining using agents. Int. J. Adv. Technol. Eng. Res. (IJATER) 3(5), 27–33 (2013)

    Google Scholar 

  • F. Stahl, M.M. Gaber, P. Aldridge, D. May, H. Liu, M. Bramer, S.Y. Philip, Homogeneous and heterogeneous distributed classification for pocket data mining, in Transactions on Large-Scale Data-and Knowledge-Centered Systems V (Springer, Berlin, Heidelberg, 2012), pp. 183–205

    Google Scholar 

  • S. Sharmila, S. Vijayarani, Association rule hiding using firefly optimization algorithm, In International Conference on Intelligent Systems Design and Applications (Springer, Cham, Dec. 2018), pp. 699–708

    Google Scholar 

  • W. Shen, et al. Applications of agent-based systems in intelligent manufacturing: an updated review. Adv. Eng. Inf. 20.4, 415–431 (2006)‏

    Google Scholar 

  • C.S. Shih, Cooperative Adaptive Control for Multi-Agent Systems (2018)

    Google Scholar 

  • A.Q. Santos, R.M. Monaro, D.V. Coury, M. Oleskovicz, M., A new real-time multi-agent system for under frequency load shedding in a smart grid context. Electric Power Syst. Res. 174, 105851 (2019)

    Google Scholar 

  • S. Seng, K.K. Li, W.L. Chan, Z. Xiangjun, D. Xianzhong, Agent-based Self-healing Protection System, in IEEE transactions on Power Delivery, vol. 21, Issue 02, Apr. 2006

    Google Scholar 

  • G. Tsoumakas, I. Vlahavas, Distributed data mining, in Database Technologies: Concepts, Methodologies, Tools, and Applications (IGI Global, 2009), pp. 157–164

    Google Scholar 

  • Q. Tong, X. Li, B. Yuan, Efficient distributed clustering using boundary information. Neurocomputing 275, 2355–2366 (2018)

    Article  Google Scholar 

  • The FIPA Specifications. www.fipa.org

  • S. Uppoor, M. Fiore, Large-scale urban vehicular mobility for networking research. in Proceedings of the IEEE Vehicular Networking Conference (VNC), Nov. 2011, pp. 62–69

    Google Scholar 

  • W. Van Der, M. Woolridge, Multi-Agent systems. Handbook of Knowledge Representation.Elsevier B.V. 2007.M (2007)

    Google Scholar 

  • J. Vrancken, M.D.S. Soares, A real-life test bed for multi-agent monitoring of road network performance. Int. J. Crit. Infrastruct. 5(4), 357–367 (2009)

    Article  Google Scholar 

  • X. Wu, X. Zhu, G.Q. Wu, W. Ding, Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2013)

    Google Scholar 

  • F. Wang, J. Sun, Survey on distance metric learning and dimensionality reduction in data mining. Data Min. Knowl. Disc. 29(2), 534–564 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  • T.Y. Wu, J.C.W. Lin, Y. Zhang, C.H. Chen, A grid-based swarm intelligence algorithm for privacy-preserving data mining. Appl. Sci. 9(4), 774 (2019)

    Article  Google Scholar 

  • O. Wangapisit, E. Taniguchi, J.S. Teo, A.G. Qureshi, Multi-agent systems modelling for evaluating joint delivery systems. Procedia Soc. Behav. Sci. 125, 472–483 (2014)

    Article  Google Scholar 

  • G. Wen, G. Hu, W. Yu, J. Cao, G. Chen, Consensus tracking for higher-order multi-agent systems with switching directed topologies and occasionally missing control inputs. Syst. Control Lett. 62(12), 1151–1158 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  • M. Wooldridge, An Introduction to Multiagent Systems (Wiley, NJ, 2008)

    Google Scholar 

  • G. Weiss, Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence (MIT Press, Cambridge, 1999)

    Google Scholar 

  • D. Yuan, A. Proutiere, A., G. Shi, Distributed Online Linear Regression (2019). arXiv:1902.04774.

  • D. Ye, M. Zhang, A.V. Vasilakos, A survey of self-organization mechanisms in multiagent systems (IEEE)

    Google Scholar 

  • N.-P. Yu, C.-C. Liu, Multiagent systems, in Advanced Solutions in Power Systems: HVDC, FACTS, and artificial intelligence (Wiley, Hoboken, NJ, 2016), pp. 903–930

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohammed Amin Almaiah .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Qasem, M.H., Hudaib, A., Obeid, N., Almaiah, M.A., Almomani, O., Al-Khasawneh, A. (2022). Multi-agent Systems for Distributed Data Mining Techniques: An Overview. In: Baddi, Y., Gahi, Y., Maleh, Y., Alazab, M., Tawalbeh, L. (eds) Big Data Intelligence for Smart Applications. Studies in Computational Intelligence, vol 994. Springer, Cham. https://doi.org/10.1007/978-3-030-87954-9_3

Download citation

Publish with us

Policies and ethics