A study on applying KFCM algorithm to source code mining

DOI

10.2991/citcs.2012.118How to use a DOI?

Keywords

C-means;KFCMalgorithm;source code mining

Abstract

This paper provides a algorithm, which is based on that kernelized fuzzy C-means uses on the study of source code mining, to solve the problem that the large number of quantities, multiple attributes and most of them discrete of software engineering. By using this algorithm, we can improve the efficiency of mining and seek faster and more effective cluster approaches. Meanwhile, we can also solve the problem that the KFCM algorithm can not cluster text data directly. Then we can over the defect of only being able to obtain the minimum values by integrating KFCM and genetic algorithm. Finally, the experiment shows that the improved KFCM algorithm has a good clustering performance and high efficiency on data mining

Copyright

© 2012, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yonghui Liu
AU  - Jingjing Tian
AU  - Lei Zhang
PY  - 2012/11
DA  - 2012/11
TI  - A study on applying KFCM algorithm to source code mining
BT  - Proceedings of the 2012 National Conference on Information Technology and Computer Science
PB  - Atlantis Press
SP  - 457
EP  - 459
SN  - 1951-6851
UR  - https://doi.org/10.2991/citcs.2012.118
DO  - 10.2991/citcs.2012.118
ID  - Liu2012/11
ER  -