Abstract
Multi-agent systems offer an architecture for distributed problem solving. Distributed data mining algorithms specialize on one class of such distributed problem solving tasks—analysis and modeling of distributed data. This paper offers 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 discusses 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.
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Babaoglu, O., Meling, H., Montresor, A.: Anthill: a framework for the development of agent-based peer-to-peer systems. Technical Report 9, Department of Computer Science, University of Bologna (November 2001)
Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and Issues in Data Stream Systems. In: Proceedings of the 21th ACM SIGMOD-SIGACT-SIGART Symposium on Principals of Database Systems (PODS), pp. 1–16 (2002)
Dhillon, I.S., Modha, D.S.: A data-clustering algorithm on distributed memory multiprocessors. In: Zaki, M.J., Ho, C.-T. (eds.) KDD 1999. LNCS (LNAI), vol. 1759, pp. 245–260. Springer, Heidelberg (2000)
Johnson, E., Kargupta, H.: Hierarchical Clustering From Distributed, Heterogeneous Data. In: Zaki, M., Ho, C. (eds.) Large-Scale Parallel KDD Systems. LNCS, vol. 1759, pp. 221–244. Springer, Heidelberg (1999)
Eisenhardt, M., Muller, W., Henrich, A.: Classifying Documents by Distributed P2P Clustering. In: Proceedings of Informatik 2003, GI Lecture Notes in Informatics, Frankfort, Germany (2003)
Farinelli, A., Grisetti, G., Iocchi, L., Lo Cascio, S., Nardi, D.: Design and evaluation of multi agent systems for rescue operations. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 4, pp. 3148–3143 (2003)
Fred, A., Jain, A.: Data Clustering Using Evidence Accumulation. In: Proceedings of the International Conference on Pattern Recognition 2002, pp. 276–280 (2002)
Forman, G., Zhang, B.: Distributed Data Clustering Can Be Efficient and Exact. SIGKDD Explorations 2(2), 34–38 (2000)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufman Publishers, San Francisco (2001)
Hand, D., Mannila, H., Smyth, P.: Principals of Data Mining. MIT Press, Cambridge (2001)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Berlin (2001)
Hinneburg, A., Keim, D.: An Efficient Approach to Clustering in Large Multimedia Databases with Noise. In: Proceedings of the 1998 International Confernece on Knowledge Discovery and Data Mining (KDD), pp. 58–65 (1998)
Januzaj, E., Kriegel, H.-P., Pfeifle, M.: DBDC: Density based distributed clustering. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 88–105. Springer, Heidelberg (2004)
Jouve, P., Nicoloyannis, N.: A New Method for Combining Partitions, Applications for Distributed Clustering. In: Proceedings of Workshop on Parallel and Distributed Computing for Machine Learning as part of the 14th European Conference on Machine Learning (2003)
Kahn, J., Katz, R., Pister, K.: Mobile networking for smart dust. In: ACM/IEEE Intl. Conf. on Mobile Computing and Networking, MobiCom 1999 (1999)
Kargupta, H., Chan, P. (eds.): Advances in Distributed and Parallel Knowledge Discovery. AAAI Press, Menlo Park (2000)
Kargupta, H., Sivakumar, K.: Existential Pleasures of Distributed Data Mining. In: Kargupta, H., Joshi, A., Sivakumar, K., Yesha, Y. (eds.) Data Mining: Next Generation Challenges and Future Directions. MIT/AAAI Press, Menlo Park (2004)
Kargupta, H., Bhargava, R., Liu, K., Powers, M., Blair, P., Klein, M.: VEDAS: A Mobile Distributed Data Stream Mining System for Real-Time Vehicle Monitoring. In: Proceedings of the 2004 SIAM International Conference on Data Mining (2004)
Kargupta, H., Huang, W., Sivakumar, K., Johnson, E.: Distributed clustering using collective principal component analysis. Knowledge and Information Systems Journal 3, 422–448 (2001)
Klusch, M., Lodi, S., Moro, G.: Distributed Clustering Based on Sampling Local Density Estimates. In: Proceedings of the Joint International Conference on AI, IJCAI 2003 (2003)
Lazarevic, A., Pokrajac, D., Obradovic, Z.: Distributed Clustering and Local Regression for Knowledge Discovery in Multiple Spatial Databases. In: Proceedings of the 8th European Symposium on Artificial Neural Networks, pp. 129–134 (2000)
Merugu, S., Ghosh, J.: Privacy-Preserving Distributed Clustering Using Generative Models. In: Proceedings of the IEEE Conference on Data Mining, ICDM (2003)
Ogston, E., Vassiliadis, S.: A Peer-to-Peer Agent Auction. In: First International Joint Conference on Autonomous Agents and Multi-Agent Systems, pp. 150–159 (2002)
Park, B., Kargupta, H.: Distributed Data Mining: Algorithms, Systems, and Applications. In: Ye, N. (ed.) The Handbook of Data Mining, pp. 341–358. Lawrence Erlbaum Associates, Mahwah (2003)
Provost, F.: Distributed Data Mining: Scaling Up and Beyond. In: Kargupta, H., Joshi, A., Sivakumar, K., Yesha, Y. (eds.) Advances in Distributed and Parallel Knowledge Discovery, pp. 3–27. MIT/AAAI Press, Menlo Park (2000)
Samatova, N., Ostrouchov, G., Geist, A., Melechko, A.: RACHET: An Efficient Cover- Based Merging of Clustering Hierarchies from Distributed Datasets. Distributed and Parallel Databases 11(2), 157–180 (2002)
Sharples, S., Lindemann, C., Waldhorst, O.: A Multi-Agent Architecture For Intelligent Building Sensing and Control. In: International Sensor Review Journal (1999)
Soh, L.-K., Tsatsoulis, C.: Reflective Negotiating Agents for Real-Time Multisensor Target Tracking. In: International Joint Conference On Artificial Intelligence (2001)
Strehl, A., Ghosh, J.: Cluster Ensembles – A Knowledge Reuse Framework for Combining Multiple Partitions. Journal of Machine Learning Research 3, 583–617 (2002)
Topchy, A., Jain, A., Punch, W.: Combining Multiple Weak Clusterings. In: Proceedings of the IEEE Conference on Data Mining, ICDM (2003)
Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufman Publishers, San Fransisco (1999)
Yu, B., Singh, M.: Emergence of Agent-Based Referral Networks. In: Proceedings of First International Joint Conference on Autonomous Agents and Multi-Agent Systems, pp. 1208–1209 (2002)
Zaki, M.: Parallel and Distributed Association Mining: A Survey. IEEE Concurrency 7(4), 14–25 (1999)
Zaki, M.: Parallel and Distributed Data Mining: An Introduction. In: Zaki, M., Ho, C.-T. (eds.) Large-Scale Parallel Data Mining (Lecture Notes in Artificial Intelligence 1759), pp. 1–23. Springer, Heidelberg (2000)
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Giannella, C., Bhargava, R., Kargupta, H. (2004). Multi-agent Systems and Distributed Data Mining. In: Klusch, M., Ossowski, S., Kashyap, V., Unland, R. (eds) Cooperative Information Agents VIII. CIA 2004. Lecture Notes in Computer Science(), vol 3191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30104-2_1
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DOI: https://doi.org/10.1007/978-3-540-30104-2_1
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