REFERENCES

Chapter V - Parallel and Distributed Data Mining through Parallel Skeletons and Distributed Objects
Data Mining: Opportunities and Challenges
by John Wang (ed)
Idea Group Publishing 2003

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Agrawal, R., Mannila, H., Ramakrishnan, S., Toivonen, H., &Verkamo, A.I. (1996). Fast discovery of association rules. In U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy (eds.), Advances in knowledge discovery and data mining, pp. 307 328. Cambridge, MA: AAAI Press / MIT Press.

Agrawal, R., &Shafer, J. (1996). Parallel mining of association rules, IEEE Transactions on Knowledge and Data Engineering 8 (6), 962 969.

Au, P., Darlington, J., Ghanem, M., Guo, Y., To, H.W., &Yang, J. (1996). Coordinating heterogeneous parallel computation. In L. Bouge, P. Fraigniaud, A. Mignotte & Y. Roberts (eds.), Europar '96, Vol. 1124 of Lecture Notes in Computer Science, Berlin: Springer-Verlag.

Bertchold, S., Keim, D. A., &Kriegel, H.-P. (1996). The X-Tree: An index structure for high-dimensional data. In Proceedings of the 22nd International Conference on Very Large Data Bases, Morgan Kaufmann Publishers, pp. 28 39.

Beyer, K., Goldstein, J., Ramakrishnan, R., &Shaft, U. (1999). When is "nearest neighbor" meaningful? In C. Beeri, & P. Buneman (eds.), Database Theory - ICDT'99 7th International Conference, Vol. 1540 of Lecture Notes in Computer Science, pp. 217 235. Berlin: Springer-Verlag.

Carletti, G. &Coppola, M. (2002). Structured parallel programming and shared projects: Experiences in data mining classifiers. In G.R. Joubert, A. Murli, F.J. Peters, & M. Vanneschi (Ed.), Parallel Computing, Advances and Current Issues, Proceedings of the ParCo 2001International Conference. London: Imperial College Press.

Cole, M. (1989). Algorithmic skeletons: Structured management of parallel computations. Research Monographs in Parallel and Distributed Computing. London: Pitman.

Coppola, M. &Vanneschi, M. (2002). High-performance data mining with Skeleton-based structured parallel programming. In Parallel Computing, special issue on Parallel Data Intensive Computing, 28(5), 793 813.

Danelutto, M. (2001). On Skeletons and design patterns. To appear in Parallel Computing, Advances and Current Issues, Proceedings of ParCo 2001 International Conference. London: Imperial College Press.

Ester, M., Kriegel, H.-P., Sander, J., &Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In E. Simoudis, J. Han & U. Fayyad (Eds.), Proceedings of KDD 96, AAAI Press, pp.226 231.

Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., &Uthurusamy, R. (eds.) (1996). Advances in knowledge discovery and data mining. Cambridge, MA: AAAI Press/MIT Press.

Freitas, A. A. &Lavington, S.H. (1998). Mining very large databases with parallel processing. Boston, MA: Kluwer Academic Publisher.

Joshi, M. V., Han, E.-H., Karypis, G., &Kumar, V. (2000). Efficient parallel algorithms for mining associations. In M. J. Zaki & C.-T. Ho (eds.), Large-scale parallel data mining. Vol. 1759 of Lecture Notes in Artificial Intelligence. New York: Springer.

Joshi, M. V., Karypis, G. &Kumar, V (1998). ScalParC: A new scalable and efficient parallel classification algorithm for mining large datasets. In Proceedings of 1998 International Parallel Processing Symposium, IEEE CS Press, pp. 573 579.

Maniatty, W. A. &Zaki, M. J. (2000). A requirement analysis for parallel KDD Systems. In J. Rolim et al. (eds.) Parallel and distributed processing, Volume 1800 of Lecture Notes in Computer Science. Berlin: Springer-Verlag.

Quinlan, J. (1993). C4.5: Programs for machine learning. San Mateo, CA: MorganKaufmann.

Savasere, A., Omiecinski, E., &Navathe, S. (1995). An efficient algorithm for mining association rules in large databases. In U. Dayal, P. Gray, and S. Nishio (eds.), Proceedings of 21st International Conference on Very Large Data Bases -VLDB '95 Zurich, pp. 432 444. San Francisco: Morgan Kaufmann.

Serot, J., Ginhac, D., Chapuis, R., &Derutin, J. (2001). Fast prototyping of parallel vision applications using functional skeletons. Machine Vision and Applications, 12, 217 290.

Shafer, J., Agrawal, R., &Mehta, M. (1996). SPRINT: A scalable parallel classifier for data mining. In Proceedings of the 22nd International Conference on Very Large Data Bases -VLDB '96., Morgan Kaufmann, pp. 544 555.

Skillicorn, D. B., &Talia, D. (1998). Models and languages for parallel computation. ACM Computing Surveys. 30(2) 123 169.

Sreenivas, M.K., AlSabti, K., &Ranka, S. (2000). Parallel out-of-core decision tree classifiers. In H. Kargupta & P. Chan (eds.), Advances in distributed and parallel knowledge discovery. Cambridge, MA: AAAI/MIT Press.

Srivastava, A., Han, E.-H., Kumar, V., &Singh, V. (1999). Parallel formulations of decision-tree classification algorithms. Data Mining and Knowledge Discovery: An International Journal, 3(3) 237 261.

Vanneschi, M. (1998a). PQE2000: HPC tools for industrial applications. IEEE Concurrency: Parallel, Distributed & Mobile Computing, 6(4) 68 73.

Vanneschi, M. (1998b). Heterogeneous HPC environments. In D. Pritchard & J. Reeve (eds.), Euro-Par '98 Parallel Processing, Vol. 1470 of Lecture Notes in Computer Science. Berlin: Springer-Verlag.

Vanneschi, M. (2002). ASSIST: A programming environment for parallel and distributed portable applications. Internal Report, ASI-PQE2000 Project, January. Submitted for publication.

Vitter, J. S, (2001). External memory algorithms and data structures: Dealing with MASSIVE DATA. ACM Computing Surveys, 33 (2) 209 271.

Xu, X., Jager, J., &Kriegel, H.-P. (1999). A fast parallel clustering algorithm for large spatial databases. Data Mining and Knowledge Discovery: An International Journal, 3(3) 263 290.

Zaki, M. J. (2000). Scalable algorithms for association Mining. IEEE Transactions on Knowledge and Data Engineering, 12, 372 390.

Zaki, M. J. &Ho, C.-T. (2000). Large scale parallel data mining. Vol. 1759 of Lecture Notes in Artificial Intelligence. Berlin: Springer-Verlag.

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