By Sushmita Mitra
* First name to ever current smooth computing techniques and their software in info mining, in addition to the conventional hard-computing methods * Addresses the foundations of multimedia facts compression options (for photo, video, textual content) and their position in information mining * Discusses ideas and classical algorithms on string matching and their function in facts mining
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Extra resources for Data Mining Multimedia, Soft Computing, and Bioinformatics
Operational update of data does not occur in the data warehouse environment. APPLICATIONS AND CHALLENGES 25 It does not require transaction processing, recovery, and concurrency control mechanisms. It requires only two operations, namely, initial loading of data and its access. Traditional heterogeneous databases build wrappers or mediators on top of the databases and adopt a query-driven approach. When a query is posed to a client site, a meta-dictionary is used to translate the query into a form appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set.
Data mining tools are often only a part of the entire decision-making system. It is desirable that they integrate smoothly, both with the database and the final decision-making procedure. 9. Compression. Storage of large multimedia databases is often required to be in compressed form. Hence the development of compression technology, particularly suitable for data mining, is required. It would be even more beneficial if data can be accessed in the compressed domain . 10. Human Perceptual aspects for data mining.
33. J. Zhang, W. Hsu, and M. L. Lee, "Image mining: Issues, frameworks and techniques," in Proceedings of the 2nd International Workshop on Multimedia Data Mining and the ACM SIGKDD Conference (MDM/KDD '01) (San Francisco), pp. 13-20, August 2001. 34. A. Vailaya, A. T. Figueiredo, A. K. Jain, and H. J. Zhang, "Image classification for content-based indexing," IEEE Transactions on Image Processing, vol. 10, pp. 117-130, 2001. 35. J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles. London: Addison-Wesley, 1974.