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Comparative Analysis of Clustering Algorithms Applied to the Classification of Bugs

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Book cover Neural Information Processing (ICONIP 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7667))

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Abstract

This paper presents a study of clustering algorithms in bug classification for a company from a database that contains a description each bug. It is made a comparison these algorithms using a sample of the database of this company. Considering that the classification will encourage the decision process of the organization as the result of the efficiency and reliability increase, this study will conduct an investigation to identify, among the techniques employed, one that will produce satisfactory results for the company, so to provide a set of information that are relevant to strategic decision making.

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References

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Author information

Authors and Affiliations

  1. Informatics Center, Fedederal Universitity of Pernambuco, Recife, Pernambuco, Brazil

    Anderson Santana, Jackson Silva, Patrícia Muniz, Fabricio Araújo & Renata Maria Cardoso R. de Souza

Authors
  1. Anderson Santana
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  2. Jackson Silva
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  3. Patrícia Muniz
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  4. Fabricio Araújo
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  5. Renata Maria Cardoso R. de Souza
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Editor information

Editors and Affiliations

  1. Texas A&M University at Qatar, Education City, P.O. Box 23874, Doha, Qatar

    Tingwen Huang

  2. Department of Control Science and Engineering, Huazhong University of Science and Technology, 1037 Luoyu Road, 430074, Wuhan, Hubei, China

    Zhigang Zeng

  3. College of Computer Science, Chongqing University, 174 Shazhengjie Street, 400044, Chongqing, China

    Chuandong Li

  4. Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China

    Chi Sing Leung

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© 2012 Springer-Verlag Berlin Heidelberg

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Santana, A., Silva, J., Muniz, P., Araújo, F., de Souza, R.M.C.R. (2012). Comparative Analysis of Clustering Algorithms Applied to the Classification of Bugs. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_70

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  • DOI: https://doi.org/10.1007/978-3-642-34500-5_70

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34499-2

  • Online ISBN: 978-3-642-34500-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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