KFCM Algorithm Based on the Source Code Mining Method Study

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.