摘要
针对传统K-medoids聚类算法对初始聚类中心敏感、收敛速度缓慢以及聚类精度不够高等缺点,提出一种基于改进粒计算、粒度迭代搜索策略和优化适应度函数的新算法。该算法利用粒计算思想在有效粒子中选择K个密度大且距离较远的粒子,选择其中心点作为K个聚类初始中心点;并在对应的K个有效粒子中进行中心点更新,来减少迭代次数;采用类间距离和类内距离优化适应度函数来提高聚类的精度。实验结果表明:该算法在UCI多个标准数据集中测试,在有效缩短迭代次数的同时提高了算法聚类准确率。
Due to the disadvantages such as sensitive to the initial selection of the center, slow convergent speed and poor accuracy in traditional K-medoids clustering algorithm, a novel K-medoids algorithm based on improved Granular Computing （GrC）, granule iterative search strategy and a new fitness function was proposed in this paper. The algorithm selected K granules using the granular computing thinking in the high-density area which were far apart, selected its center point as the K initial cluster centers, and updated K center points in candidate granules to reduce the number of iterations. What＇s more, a new fitness function was presented based on between-class distance and within-class distance to improve clustering accuracy. Tested on a number of standard data sets in UCI, the experimental results show that this new algorithm reuduces the number of iterations effectively and improves the accuracy of clustering.
出处
《计算机应用》
CSCD
北大核心
2014年第7期1997-2000,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(11171095
71371065)
湖南省自然科学衡阳联合基金资助项目(10JJ8008)
湖南省科技计划项目(2013SK3146)
湖南省研究生科研创新项目(CX2014B386)
