Variable penalty factors: a new GEP automatic clustering algorithm
Yan Chen, Kangshun Li, Haohua Huang · International Journal of Computational Science and Engineering · 2017
The clustering algorithm is considered as an important and basic method in the field of data mining on interdisciplinary researches. Various problems such as sensitive selection of initial clustering centre, easy to fall into local optimal solution, poor universal search capacity and requiring prior knowledge for determining numbers of clusters still exist in the traditional clustering algorithm. A gene expression programming (GEP) automatic clustering algorithm with variable penalty factors is adopted in this paper, featuring combination of penalty factors and GEP clustering algorithm, no requirements for prior knowledge on the data set, automatic division of clusters and better solution for the impact of isolated points and noise points. The simulation experiment makes further proof of the effectiveness of the algorithm in this paper.