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Advances in Computer and Computational Sciences pp 87–99Cite as

A Rule Extraction for Outsourced Software Project Risk Classification

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 554))

Abstract

A rule extraction algorithm based on K-means clustering with interval-valued intuitionistic fuzzy sets (IVIFS) information, which is the combination of K-means clustering and IVIFS, is proposed in this paper. First, we introduce IVIFS and its distance. Second, we introduce IVIFS method and its application to the classification of software project. Finally, we present a rule extraction model according to IVIFS fuzziness and its K-means clustering, and apply them to pattern classification of outsourced software project risk to demonstrate the advantages of this model. The experimental results show that the rules from IVIFS model are better than that from the conventional K-means clustering model in rule extraction, and the prediction effect from the former is more effective than that from the latter. According to this combinational rule extraction method, based on database from a special and professional investigation for Chinese small and medium software-outsourced enterprise, we obtain some valuable, realistic and available project development risks decision rules.

2016 International Conference on Computer, Communication & Computational Sciences [IC4S 2016]

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Acknowledgements

This research is supported by National Natural Science Foundation of China (No. 71271061), Natural Science Projects (No. 2014A030313575, 2016A030313688) and Soft Science funds (No. 2015A070704051, 2015A070703019) and Social Sciences project (No. GD12XGL14) and Education Department Project (No. 2013KJCX0072) of Guangdong Province, Social Sciences funds of Guangzhou (No. 14G41), Team Fund (No. TD1605) and Innovation Project (No. 15T21) and Advanced Education Project (No. 16Z04, GYJYZDA14002) of GDUFS.

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Authors and Affiliations

  1. School of Economics and Trade, Guangdong University of Foreign Studies, Guangzhou, 510006, China

    Zhen-hua Zhang, Kuixi Xiao, Shenguo Yuan & Zhao Chen

  2. Institute of Big Data and Decision, Jinan University, Guangzhou, 510632, China

    Yong Hu

Authors
  1. Zhen-hua Zhang
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  2. Yong Hu
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  3. Kuixi Xiao
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  4. Shenguo Yuan
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  5. Zhao Chen
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Corresponding author

Correspondence to Zhen-hua Zhang .

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Editors and Affiliations

  1. Department of Mathematics and Computer Science, University of Missouri, St. Louis, Missouri, USA

    Sanjiv K. Bhatia

  2. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    Krishn K. Mishra

  3. CSED, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  4. Department of Computer Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India

    Vivek Kumar Singh

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Zhang, Zh., Hu, Y., Xiao, K., Yuan, S., Chen, Z. (2018). A Rule Extraction for Outsourced Software Project Risk Classification. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 554. Springer, Singapore. https://doi.org/10.1007/978-981-10-3773-3_10

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  • DOI: https://doi.org/10.1007/978-981-10-3773-3_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3772-6

  • Online ISBN: 978-981-10-3773-3

  • eBook Packages: EngineeringEngineering (R0)

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