Improvement of Weighted Euclidean Distance Clustering Algorithm
ZHU Lizhi
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 .
Keywords：weighted Euclidean distancesimilarityweightattributeclustering
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 421-424 )