k-Attractors: A Clustering Algorithm for Software Measurement Data Analysis

@article{Kanellopoulos2007kAttractorsAC,
  title={k-Attractors: A Clustering Algorithm for Software Measurement Data Analysis},
  author={Yiannis Kanellopoulos and Panagiotis Antonellis and Christos Tjortjis and Christos Makris},
  journal={19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007)},
  year={2007},
  volume={1},
  pages={358-365}
}
Clustering is particularly useful in problems where there is little prior information about the data under analysis. This is usually the case when attempting to evaluate a software system's maintainability, as many dimensions must be taken into account in order to reach a conclusion. On the other hand partitional clustering algorithms suffer from being sensitive to noise and to the initial partitioning. In this paper we propose a novel partitional clustering algorithm, k-Attractors. It employs… CONTINUE READING
Highly Cited
This paper has 20 citations. REVIEW CITATIONS

From This Paper

Topics from this paper.
14 Citations
12 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 14 extracted citations

Similar Papers

Loading similar papers…