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Combining an Order-Semisensitive Text Similarity and Closest Fit Approach to Textual Missing Values in Knowledge Discovery

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3682))

Abstract

The ubiquity of textual information nowadays reflects its great significance in knowledge discovery. However, effective usage of these textual materials is always hampered by data incompleteness in real-life applications. In this paper, we apply a closest fit approach to attack textual missing values. To evaluate the closeness of texts in this application, we present an order perspective of text similarity and propose a hybrid order-semisensitive measure, M-similarity, to capture the proximity of texts. This measure combines single item matching, maximum sequence matching and potential matching and get a proper balance between usage of sequence information and efficiency. We incorporate M-similarity into two closest fit methods to missing values in textual attributes and evaluate them on data sets of Traditional Chinese Medicine (TCM). Experimental results illustrate the effectiveness of these methods with M-similarity.

This work is supported by China 973 project: 2003CB317006

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Author information

Authors and Affiliations

  1. College of Computer Science, Zhejiang University, Hangzhou, 310027, P.R. China

    Yi Feng, Zhaohui Wu & Zhongmei Zhou

Authors
  1. Yi Feng
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  2. Zhaohui Wu
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  3. Zhongmei Zhou
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Editor information

Editors and Affiliations

  1. School of Business, La Trobe University, 3086, Melbourne, Victoria, Australia

    Rajiv Khosla

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2005 Springer-Verlag Berlin Heidelberg

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Feng, Y., Wu, Z., Zhou, Z. (2005). Combining an Order-Semisensitive Text Similarity and Closest Fit Approach to Textual Missing Values in Knowledge Discovery. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552451_130

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  • DOI: https://doi.org/10.1007/11552451_130

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28895-4

  • Online ISBN: 978-3-540-31986-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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