摘要
随着云计算、移动计算机互联网技术的快速发展,海量数据分析已经成为企业战略决策、营销推广的基础,海量数据挖掘越来越重要,传统的聚类分析已经不能满足要求,例如数据划分绝对、准确率低等。本文引入模糊聚类思想,提出了一种模糊K均值算法,基于隶属度关系进行分类,在很大程度上增加数据挖掘的正确率。
The paper, based on the idea of fuzzy clustering, proposed a fuzzy algorithm for K-means to guarantee the accuracy of data mining, wherein classification is made according to the degree of membership when conventional clustering analysis, owing to absolute data classification and low precision, has proved not able to cope with massive data mining that has already become a crucial factor in enterprise strategic decision-making and marketing along with the rapid development of cloud computation and mobile internet technology.
作者
张思维
郭秀娟
ZHANG Si-wei;GUO Xiu-juan(School of Electricity and Computer Science at Jilin Jianzu University, Changchun Jilin 130118,China)
出处
《吉林工程技术师范学院学报》
2019年第4期86-88,共3页
Journal of Jilin Engineering Normal University
关键词
模糊数学
K均值
硬聚类
隶属度
Fuzzy Mathematics
K-means
Hard Clustering
Degree of Membership
