CAJ | 학술논문

针对K-均值算法对初始值敏感和易陷入局部最优的缺点，提出了一种基于改进差分进化的K-均值聚类算法。该算法通过引入基于Laplace分布的变异算子和Logistic变尺度混沌搜索来增强全局寻优能力。实验结果表明，该算法能够较好地克服传统K-均值算法的缺点，具有较好的搜索能力，且算法的收敛速度较快，鲁棒性较强。
침대K-균치산법대초시치민감화역함입국부최우적결점，제출료일충기우개진차분진화적K-균치취류산법。해산법통과인입기우Laplace분포적변이산자화Logistic변척도혼돈수색래증강전국심우능력。실험결과표명，해산법능구교호지극복전통K-균치산법적결점，구유교호적수색능력，차산법적수렴속도교쾌，로봉성교강。
The conventional k-means algorithms are sensitive to the initial cluster centers, and tend to be trapped by local opti-ma. To resolve these problems, a novel k-means clustering algorithm using enhanced differential evolution technique is proposed in this paper. This algorithm improves the global search ability by applying Laplace mutation operator and variable-scale Logistic chaotic searching. Numerical experiments show that this algorithm overcomes the disadvantages of the conventional k-means al-gorithms, and improves search ability with higher accuracy, faster convergence speed and better robustness.