Approaching Software Cost Estimation Using an Entropy-Based Fuzzy k-Modes Clustering Algorithm

@inproceedings{Papatheocharous2009ApproachingSC,
  title={Approaching Software Cost Estimation Using an Entropy-Based Fuzzy k-Modes Clustering Algorithm},
  author={Efi Papatheocharous and Andreas S. Andreou},
  booktitle={AIAI Workshops},
  year={2009}
}
A new software cost estimation approach is proposed in this paper, which attempts to cluster empirical, non-homogenous project data samples via an entropy-based fuzzy k-modes clustering algorithm. The target is to identify groups of projects sharing similar characteristics in terms of cost attributes or descriptors, and utilise this grouping information to provide estimations of the effort needed for a new project that is classified in a certain group. The effort estimates produced address the… CONTINUE READING

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Fuzzy Set

  • L. A. Zadeh
  • Information and Control,
  • 2009
2 Excerpts

A comparative evaluation on the accuracies of software effort estimates from clustered data

  • Huang, S.-J, Chiu, N.-H, Liu, Y.-J
  • Information of Software Technology 50,
  • 2008
1 Excerpt

The role of outcome feedback in improving the uncertainty assessment of software development effort estimates

  • T. M. Gruschke, M. Jørgensen
  • In ACM Transactions of Software Engineering…
  • 2008
1 Excerpt

Software Engineering. Addison-Wesley Longman Publishing Co., Inc

  • I. Sommerville
  • 2007
1 Excerpt

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