计算机与数字工程2016，Vol.44Issue(3)：421-424,4.DOI:10.3969/j.issn.1672-9722.2016.03.009
一种加权欧氏距离聚类算法的改进磁
Improvement of Weighted Euclidean Distance Clustering Algorithm
朱俚治1
作者信息
- 1. 南京航空航天大学信息中心南京 210016
- 折叠
摘要
Abstract
Clustering algorithm is an unsupervised learning algorithm ,which can cluster the data with strong similarity to a family ,and can divide the data into different groups .Clustering algorithms can be classified into the traditional cluste‐ring algorithm and the non traditional clustering algorithm .The traditional clustering algorithms are based on clustering algo‐rithm and hierarchical clustering algorithm .When clustering is used to cluster by dividing method and hierarchy process ,the distance between the attributes of clustering objects is calculated .So the Euclidean distance formula and the weighted Euclid‐ean distance formula are used in the two clustering algorithms .Weighted Euclidean distance formula in the clustering object class attribute weights of reunion ,so this paper from the object cluster similarity and the properties of an object correspond‐ing to the weights ,the two aspects to consider clustering success probability .The algorithm proposed by this paper is that if a clustering object has a number of attributes ,then it first calculates the similarity of the clustering object attributes ,and then according to the weight of the attributes corresponding to the weight is the key ,then the success rate of the object clus‐tering is higher .关键词
加权欧氏距离/相似度/权重/属性/聚类Key words
weighted Euclidean distance/similarity/weight/attribute/clustering分类
信息技术与安全科学引用本文复制引用
朱俚治..一种加权欧氏距离聚类算法的改进磁[J].计算机与数字工程,2016,44(3):421-424,4.
