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Data Mining and Knowledge Discovery Handbook, 2 Edition part 111. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 57 Multimedia Data Mining Zhongfei Mark Zhang and Ruofei Zhang 1 SUNY at Binghamton NY 13902-6000 zhongfei@cs.binghamton.edu 2 Yahoo Inc. Sunnyvale CA 94089 rzhang@yahoo-inc.com Summary. Each chapter should be preceded by an abstract 10-15 lines long that summarizes the content. The abstract will appear online at www.SpringerLink.com and be available with unrestricted access. This allows unregistered users to read the abstract as a teaser for the complete chapter. As a general rule the abstracts will not appear in the printed version of your book unless it is the style of your particular book or that of the series to which your book belongs. Please use the starred version of the new Springer abstract command for typesetting the text of the online abstracts cf. source file of this chapter template abstract and include them with the source files of your manuscript. Use the plain abstract command if the abstract is also to appear in the printed version of the book. 57.1 Introduction Multimedia data mining as the name suggests presumably is a combination of the two emerging areas multimedia and data mining. However multimedia data mining is not a research area that just simply combines the research of multimedia and data mining together. Instead the multimedia data mining research focuses on the theme of merging multimedia and data mining research together to exploit the synergy between the two areas to promote the understanding and to advance the development of the knowledge discovery in multimedia data. Consequently multimedia data mining exhibits itself as a unique and distinct research area that synergistically relies on the state-of-the-art research in multimedia and data mining but at the same time fundamentally differs from either multimedia or data mining or a simple combination of the two areas. Multimedia and data mining are two very interdisciplinary and multidisciplinary areas. Both areas started in early 1990s with only a very short history. Therefore both