Abstract
One key aspect of exploiting the huge amount of autonomous and heterogeneous data sources in the Internet is not only how to retrieve, collect and integrate relevant information but to discover previously unknown, implicit and valuable knowledge. In recent years several approaches to distributed data mining and knowledge discovery have been developed, but only a few of them make use of intelligent agents. This paper is intended to argue for the potential added value of using agent technology in the domain of knowledge discovery.We briefly review and classify existing approaches to agent-based distributed data mining, propose a novel approach to distributed data clustering based on density estimation, and discuss issues of its agent-oriented implementation.
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References
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. AAAI Press/MIT Press (1996)
Chen, M. S., Han, J., Yu, P. S.: Data mining: an overview from a database perspective. IEEE Trans. On Knowledge And Data Engineering 8 (1996) 866–883
Kargupta, H., Park, B., Hershberger, D., Johnson, E.: 5, Collective Data Mining: A New Perspective Toward Distributed Data Mining. In: Advances in Distributed and Parallel Knowledge Discovery. AAAI/MIT Press (2000) 131–178
Moro, G., Sartori, C.: Incremental maintenance of multi-source views. In: M. E. Orlowska, J. Roddick Edition, Proceedings of 12th Australasian Database Conference, ADC 2001, Brisbane, Queensland, Australia, IEEE Computer Society (2001) 13–20
Dhar, V., Chou, D., Provost, F. J.: Discovering interesting patterns for investment decision making with glower-a genetic learner. Data Mining and Knowledge Discovery 4 (2000) 251–280
Klusch, M.: Information agent technology for the internet: A survey. Data and Knowledge Engineering, Special Issue on Intelligent Information Integration. Elsevier Science 36 (2001) 337–372
Wooldridge, M.: Intelligent agents: The key concepts. In Marík, V., Stepánková, O., Krautwurmova, H., Luck, M., eds.: Multi-Agent-Systems and Applications. Volume 2322 of LNCS., Springer-Verlag (2002) 3–43
Kargupta, H., Hamzaoglu, I., Stafford, B.: Scalable, distributed data mining using an agent-based architecture. In D. Heckerman, H. Mannila, D. Pregibon, R. Uthurusamy, eds.: Proc. 3rd International Conference on Knowledge Discovery and Data Mining, Newport Beach, California, USA, AAAI Press (1997) 211–214
Stolfo, S. J., Prodromidis, A. L., Tselepis, S., Lee, W., Fan, D. W., Chan, P. K.: JAM: Java agents for meta-learning over distributed databases. In David Heckerman, Heikki Mannila, D. P., ed.: Proc. Third International Conference on Knowledge Discovery and Data Mining (KDD-97), Newport Beach, California, USA, AAAI Press (1997) 74–81
Bailey, S., Grossman, R., Sivakumar, H., Turinsky, A.: Papyrus: a system for data mining over local and wide area clusters and super-clusters. In: Proc. Conference on Supercomputing, ACM Press (1999) 63
Papazoglou, M. P., Schlageter, G.: Cooperative Information Systems-Trends and Directions. Academic Press Ltd, London, UK (1998)
Zhong, N., Matsui, Y., Okuno, T., Liu, C.: Framework of a multi-agent kdd system. In Yin, H., Allinson, N. M., Freeman, R., Keane, J. A., Hubbard, S. J., eds.: Proc. of Intelligent Data Engineering and Automated Learning-IDEAL 2002, Third International Conference, Manchester, UK. Volume 2412 of Lecture Notes in Computer Science., Springer-Verlag (2002) 337–346
Sen, S., Biswas, A., Gosh, S.: Adaptive choice of information sources. In: Proc. 3rd International workshop on Cooperative Information Agents, Springer (1999)
Theilmann, W., Rothermel, K.: Disseminating mobile agents for distributed information filtering. In: Proc. of 1st International Sympos. on Mobile Agents, IEEE Press (1999) 152–161
Prasad, M., Lesser, V.: Learning situation-specific coordinating in cooperative multi-agent systems. Autonomous Agents and Multi-Agent Systems (1999)
Grabmeier, J., Rudolph, A.: Techniques of cluster algorithms in data mining. Data Mining and Knowledge Discovery 6 (2002) 303–360
Ankerst, M., Breunig, M. M., Kriegel, H. P., Sander, J.: OPTICS: Ordering points to identify the clustering structure. In: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, Philadephia, PA (1999) 49–60
Ester, M., Kriegel, H. P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, OR (1996) 226–231
Hinneburg, A., Keim, D. A.: An efficient approach to clustering in large multimedia databases with noise. In: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), New York City, New York, USA, AAAI Press (1998) 58–65
Schikuta, E.: Grid-clustering: An efficient hierarchical clustering method for very large data sets. In: Proceedings of the 13th International Conference on Pattern Recognition, IEEE (1996) 101–105
Silverman, B. W.: Density Estimation for Statistics and Data Analysis. Chapman and Hall, London (1986)
Johnson, E., Kargupta, H.: Collective, hierarchical clustering from distributed heterogeneous data. In Zaki, M., Ho, C., eds.: Large-Scale Parallel KDD Systems. Lecture Notes in Computer Science. Springer-Verlag (1999) 221–244
Kargupta, H., Huang, W., Krishnamoorthy, S., Johnson, E.: Distributed clustering using collective principal component analysis. Knowledge and Information Systems Journal 3 (2000) 422–448
Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: An efficient data clustering method for very large darabases. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Canada (1996) 103–114
Ganti, V., Ramakrishnan, R., Gehrke, J., Powell, A. L., French, J. C.: Clustering large datasets in arbitrary metric spaces. In: Proceedings of the 15th International Conference on Data Engineering (ICDE 1999), Sydney, Austrialia (1999) 502–511
Ester, M., Kriegel, H. P., Sander, J., Wimmer, M., Xu, X.: Incremental clustering for mining in a data warehousing environment. In: Proceedings of the 24th International Conference on Very Large Data Bases (VLDB’98), New York City, NY (1998) 323–333
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Klusch, M., Lodi, S., Moro, G. (2003). Agent-Based Distributed Data Mining: The KDEC Scheme. In: Klusch, M., Bergamaschi, S., Edwards, P., Petta, P. (eds) Intelligent Information Agents. Lecture Notes in Computer Science(), vol 2586. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36561-3_5
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DOI: https://doi.org/10.1007/3-540-36561-3_5
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