By Sushmita Mitra
* First identify to ever current delicate computing techniques and their software in facts mining, besides the normal hard-computing ways* Addresses the foundations of multimedia info compression thoughts (for photograph, video, textual content) and their function in facts mining* Discusses rules and classical algorithms on string matching and their function in facts mining
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Extra info for Data Mining: Multimedia, Soft Computing, and Bioinformatics
Here we provide examples to show how patterns are matched in general text, as well as how they can be applied in DNA matching in Bioinformatics. The different tasks of data mining like classification, clustering and association rules are covered in Chapters 5,6, and 7, respectively. The issue of rule generation and modular hybridization, in the soft computing framework, is described in Chapter 8. Multimedia data mining, including text mining, image mining, and Web mining, is dealt with in Chapter 9.
T. M. Mitchell, "Machine learning and data mining," Communications of the ACM, vol. 42, pp. 30-36, 1999. 23. F. Provost and V. Kolluri, "A survey of methods for scaling up inductive algorithms," Data Mining and Knowledge Discovery, vol. 2, pp. 131-169, 1999. 24. T. Acharya, VLSI Algorithms and Architectures for Data Compression. D. thesis, Department of Computer Science, University of Central Florida, Orlando, FL, August 1994. 25. K. Sayood, Introduction to Data Compression. San Francisco: Morgan Kaufmann, 2000.
Nonstandard and incomplete data. The data can be missing and/or noisy. These need to be handled appropriately. 6. Mixed media data. Learning from data that are represented by a combination of various media, like (say) numeric, symbolic, images, and text. 7. Management of changing data and knowledge. Rapidly changing data, in a database that is modified or deleted or augmented, may make previously discovered patterns invalid. Possible solutions include incremental methods for updating the patterns.