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Text Mining pp 341–357Cite as

Dynamic Document Organization

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Part of the book series: Studies in Big Data ((SBD,volume 45))

Abstract

This is concerned with the process of executing the dynamic document organization system which was initially proposed by Jo in 2006.

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References

  1. Eiben, A.E., Smith J.E.: Introduction to Evolutionary Computing. Springer, Berlin (1998)

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  2. Jo, T.: The Implementation of Dynamic Document Organization Using the Integration of Text Clustering and Text Categorization, University of Ottawa (2006)

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  3. Jo, T.: Modified version of SVM for text categorization. Int. J. Fuzzy Log. Intell. Syst. 8, 52–60 (2008)

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  4. Jo, T.: Index optimization with KNN considering similarities among features. In: The Proceedings of 14th International Conference on Advances in Information and Knowledge Engineering, pp. 120–124 (2015)

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  5. Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., Watkins, C.: Text classification with string kernels. J. Mach. Learn. Res. 2, 419–444 (2002)

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  6. Marz, N., Warren, J.: Big Data: Principles and Best Practices of Scalable Real Time Data Systems. Manning, Shelter Island (2015)

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Jo, T. (2019). Dynamic Document Organization. In: Text Mining. Studies in Big Data, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-319-91815-0_16

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  • DOI: https://doi.org/10.1007/978-3-319-91815-0_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91814-3

  • Online ISBN: 978-3-319-91815-0

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