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Abstract: The main thrust of this paper is application of a novel data mining approach on the log of user's feedback to improve web multimedia information retrieval performance. A user space model was constructed based on data mining, and then integrated into the original information space model to improve the accuracy of the new information space model. It can remove clutter and irrelevant text information and help to eliminate mismatch between the page author's expression and the user's understanding and expectation. user space model was also utilized to discover the relationship between high-level and low-level features for assigning weight. The authors proposed improved Bayesian algorithm for data mining. Experiment proved that the authors' proposed algorithm was efficient.

[1]Flickner,M., Harpreet,S.S., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafner, J., Lee, D., Petkovic, D., Steele, D. and Yanker, P., 1995. Query by image and video content. IEEE Computer, 28(9):23-32.

[2]Gudivada, V.N. and Raghavan, J.V., 1995. Content-based image retrieval systems. IEEE Computer Magazine, 28(9): 18-22.

[3]Han, J.W., Meng, X.F., Wang J. and Li, S.E., 2001. Research on WEB mining. Journal of Computer Research & Development, 38: 405-414.

[4]Harman,D., Fox,E., Baeza-Yates,R.A., Lee, W.C., 1992. Inverted Files, Information Retrieval: Data Structures and Algorithms. Prentice-Hall Inc., New Jersey, p.28-43.

[5]Hiemstra,D. and Stephen,E.R., 2001. Relevance feedback for best match term weighting algorithms in information retrieval. Proc of the Second DELOS Network of Excellence Workshop on Personalization and Recommender Systems in Digital Libraries. Dublin City University, Ireland, http://www.ercim.org/publication/ws-proceedings/DelNoe02/hiemstra.pdf.

[6]Lu, Y., Hu, C.H., Zhu, X.Q., Zhang, H.J. and Yang, Q., 2000. A unified framework for semantics and feature based relevance feedback in image retrieval systems. Proc. of the 8th ACM Multimedia Conference. Los Angeles, USA, 31-37.

[7]Raghavan,V.V. and Aladdin,H., 2000. Dynamic data mining. Proc. of the 13th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, New Orleans, USA, p.220-229.

[8]Rocchio,J.J., 1971. Relevance feedback in information retrieval.: The SMART Retrieval System. Prentice Hall Inc., New Jersey, USA, p.313-323.

[9]Wang, J.C., Pan, J.G. and Zhang,F.Y., 2000. Research on web text mining. Journal of Computer Research& Development, 37(5): 513-520(in Chinese).

[10]Wu, H., Li, M.J., Zhang, H.J. and Ma, W.Y., 2002. Improving image retrieval with semantic classification using relevance feedback. Proc. of IFIP TC2/WG2.6 6th Working Conference on Visual Database Systems. Brisbane, Australia, p. 327-339.