闵可夫斯基距离
欧几里德距离
计算机科学
相似性(几何)
聚类分析
距离测量
距离测量
闵可夫斯基空间
k均值聚类
特征(语言学)
欧几里德几何
数据挖掘
相似性度量
k-中位数聚类
模糊聚类
算法
单连锁聚类
CURE数据聚类算法
相关聚类
模式识别(心理学)
人工智能
数据库扫描
树冠聚类算法
度量(数据仓库)
高维数据聚类
数学
图像(数学)
几何学
哲学
语言学
作者
P. Indira Priya,Debashis Ghosh
出处
期刊:International journal of computer applications
[Foundation of Computer Science]
日期:2012-12-18
被引量:4
摘要
distance measure for similarity estimation based on the differences is presented through our proposed algorithm. This kind of distance measurement is implemented in the K-means clustering algorithm. In this paper, a new Minkowski distance based K-means algorithm called Enhanced K-means Clustering algorithm (EKMCA) is proposed and also demonstrates the effectiveness of the distance measurement, the performance of this kind of distance and the Euclidian and Minkowski distances were compared by clustering KDD'99 Cup dataset. Experiment results show that the new distance measure can provide a more accurate feature model than the classical Euclidean and Manhattan distances.
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