ABC-KHM混合聚类算法

ABC-KHM Hybrid Clustering Algorithm

使用调和均值的KHM聚类算法,不像KM聚类算法,具有对初始值不敏感的优点。但它作为一个基于中心聚类算法,难以摆脱早熟收敛的问题。为了克服KHM算法的不足,本文提出结合ABC和KHM的ABC-KHM混合聚类算法。在混合算法中,聚类行为可以分为两个阶段:全局搜索的ABC聚类阶段和局部求精的KHM聚类阶段。通过仿真实验,并与KHM聚类算法进行了比较,结果表明:ABC-KHM混合聚类算法,不仅对聚类初始值不敏感,而且具有较快的聚类速度、良好的全局聚类效果,是一个不错的聚类算法。

The K-Harmonic Means averages of the distances from each K-means, KHM is less sensitive to （KHM） is a center-based clustering algorithm which uses the harmonic data point to the centers as components to its performance function. Unlike initial conditions. However, KHM as a center-based clustering algorithm can only generate a local optimal solution. The paper presents a hybrid clustering algorithm combining Artificial Bee Colony and K-Harmonic Means （ABC-KHM） for solving this problem. This hybrid clustering algorithm has been implemented and tested on several simulated and real datasets. The performance of this algorithm is compared with KHM. Our computational simulations reveal the ABC-KHM clustering algorithm has the advantage of is a robust clustering global searching, fast convergence and less sensitive to initial conditions. The ABC-KHM algorithm.