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A Framework of Multi Instance Objects for Resourceful Retrieval of Specific Websites
Abstract
Knowledge Discovery in Database (KDD) is the non-trivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in large data collections. However, with the growing amount of data the complexity of data objects increases as well. Multi-instance and multi-represented objects are two important types of object representation for complex objects. The main contribution of this proposed work is the development of new KDD methods for the classification and clustering of complex objects. Therefore, the proposed work introduces solutions for real world applications that are based on multi instance object representations. On the basis of these solutions, it will show that a more general object representation often provides better results for many relevant KDD applications. The first is the data mining and its uses of projected clustering for the automatic construction of product projections. The introduced solution decomposes a single part into a set of subspaces and compares them by using a metric on multi-instance of objects.
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References
Nikolaos Pappas, Andrei Popescu-Belis, “Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis” Proceedings of the 2014 Conference on Empirical Methods in Natural Language processing (EMNLP), pages 455–466, October 25-29, 2014.
Wei-Jia Zhang, Zhi-Hua Zhou, “Multi-Instance Learning with Distribution Change”, Association for the Advancement of Artificial Intelligence, 2014
Chengcui Zhang, Wei-Bang Chen, Xin Chen, Lin Yang, and John Johnstone, “A Multiple Instance Learning and Relevance Feedback Framework for Retrieving Abnormal Incidents in Surveillance Videos” ACADEMY PUBLISHER, JOURNAL OF MULTIMEDIA, VOL. 5, NO. 4, AUGUST 2010
Maron, O. and Ratan, A. L. (1998). Multiple-Instance Learning for Natural Scene Classification. In Proc. of ICML, 1998.
Rahmani, R. and Goldman, S. A. (2006). MISSL: multipleinstance semi-supervised learning. In Proc. of ICML, 2006.
Rahmani, R., Goldman, S. A., Zhang, H., Krettek, J., and Fritts, J. E. (2005). Localized content based image retrieval. In Proceedings of the 7th ACM SIGMM international Workshop on Multimedia information Retrieval, 2005.
Dietterich, T. G., Lathrop, R. H., and Lozano-Pérez, T. (1997). Solving the multiple instance problem with axisparallel rectangles. Artif. Intell. 89, 1-2 (Jan. 1997), 31-71.
Andrews, S., Tsochantaridis, I., and Hofmann, T. (2003). Support vector machines for multiple-instance learning. In Proc. of the 15th Conference of NIPS, 2002.
Bi, J., Chen, Y., and Wang, J. Z. (2005). A Sparse Support Vector Machine Approach to Region-Based Image Categorization. In Proceedings of CVPR, 2005.
Chen, Y. and Wang, J. Z. (2004). Image Categorization by Learning and Reasoning with Regions. JMLR, 2004.
Maron, O. and Lozano-Pérez, T. (1998). A framework for multiple-instance learning. In Proceedings of the 1997 Conference on Advances in Neural information Processing Systems 10 (Denver, Colorado, United States). M. I. Jordan, M. J. Kearns, and S. A. Solla, Eds. MIT Press, Cambridge, MA, 570-576.
Yang, C., Dong, M., and Hua, J. (2006). Region-based Image Annotation using Asymmetrical Support Vector Machinebased Multiple-Instance Learning. In Proc. of CVPR, 2006.
Zhang, Q., Goldman, S., Yu, W., and Fritts, J. (2002). Content-Based Image Retrieval Using Multiple-Instance Learning. In Proceeding of ICML, 2002.
Stuart Andrews, Ioannis Tsochantaridis, and Thomas Hofmann. 2003. Support vector machines for multiple-instance learning. In Advances in Neural Information Processing Systems, pages 561–568, Vancouver, British Columbia, Canada.
Andrews, S.; Tsochantaridis, I.; and Hofmann, T. 2003. Support vector machines for multiple-instance learning. In Advances in Neural Information Processing Systems 15. 561–568.
F. Bounch, F. Giannotti, C. Gozzi, G. Manco, M. Nanni, D. Pedreschi, C. Renso, S. RuggierWeb log data warehourseing and mining for intelligent web cachingJ. Data Knowledge Eng., 36 (2001), pp. 165–189 PH:S0169-023x(01)00038-6
L.D. Catledge, J. Pitkow Characterizing browsing strategies in the World Wide Web J. Comput. Networks ISDN Syst., 27 (6) (1995), pp. 1065–1073 doi: 101016/0169-7552(95)00043-7
M.K. James, R.G. Michael, A.G. James, A fuzzy K-Nearest Neighbor Algorithm. IEEE Transactions on System Man and Cybernetics, vol. SMC-15 No4.[0018-9472/85/0700-0580$01.00], 1985
E.P. Leif, K-Nearest Neighbor. Scholarpedia 4(2):1883. Downloaded 27-04-2014, @ www.google.com, 2009.
H. Paul, N. Kenta, Better Prediction of Protein Cellular Localization Sites with the K-Nearest Neighbor Classifier, ISMB-97, Proceeding of America Association for Artificial Intelligence, USA, 1997, pp. 147–152.
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