计算机工程与应用2019，Vol.55Issue(8)：27-33,7.DOI:10.3778/j.issn.1002-8331.1810-0075
改进的K-means聚类k值选择算法
Improved K-means Clustering k-Value Selection Algorithm
摘要
Abstract
In spatial clustering algorithms, the effect of clustering depends to a large extent on the choice of the best k value. In the typical K -means algorithm, the k value of clusters needs to be determined in advance, but in actual cases, the value of k is difficult to determine. The paper proposes an improved k-value selection algorithm, ET-SSE, based on the nature of exponential function, weight adjustment, bias and Elbow Method for the"elbow-point"ambiguity in the pro-cess of determining the k-value. The algorithm is tested by multiple UCI data sets and K -means clustering algorithm. The results show that the k-value selection algorithm can determine the value of key more accurately than the Elbow Method.关键词
K-均值算法/k值选择/ET-SSE算法Key words
K-means algorithm/ k-value selection/ ET-SSE algorithm分类
信息技术与安全科学引用本文复制引用
王建仁,马鑫,段刚龙..改进的K-means聚类k值选择算法[J].计算机工程与应用,2019,55(8):27-33,7.基金项目
国家自然科学基金(No.61741203,No.61866006) （No.61741203,No.61866006）
广西自然科学基金(No.2016GXNSFAA380243) （No.2016GXNSFAA380243）
广西创新驱动发展专项基金(No.桂科AA17204091) （No.桂科AA17204091）
广西南宁市科学研究与技术开发计划项目(No.20181015-5). （No.20181015-5）
