摘要
K-Means聚类算法在面对海量数据时,时间和空间的复杂性已成为K-Means聚类算法的瓶颈。在充分研究传统K-Means聚类算法的基础上,提出了基于集群环境的并行K-Means聚类算法的设计思想,给出了其加速比估算公式,并通过实验证明了该算法的正确性和有效性。
The complexity of time and spatial is becoming the difficulty of K-Means clustering algorithm while it deals with the huge amounts of data sets.Based on the study of the traditional K-Means clustering algorithm,the design concept of the parallel K-Means algorithm is discussed and a formula of the speedup ratio is proposed.The accuracy and validity of the algorithm through experiments are proved.
出处
《河南科技大学学报（自然科学版）》
CAS
2008年第4期42-45,共4页
Journal of Henan University of Science And Technology:Natural Science
基金
国家自然科学基金项目(60203018)
教育部科学研究重点项目(200202)
