文章基本信息

标题：Comparative Analysis & Evaluation of Euclidean Distance Function and Manhattan Distance Function Using K-means Algorithm
本地全文：下载
作者：Amit Singla ; Mr. Karambir
期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2012
卷号：2
期号：7
出版社：S.S. Mishra
摘要：Clustering is division of data into groups of similar objects. Each group, called a cluster, consists of objects which are similar between themselves and different as compared to objects of the other groups. In cluster, analysis is the organization of a collection of patterns into cluster based on similarity. This paper is intended to study and compare Euclidean distance function and Manhattan distance function by using k-means algorithm. This distance functions are compared according to number of iterations and within sum of squared error. Some conclusions that are extracted belong to the time complexity and accuracy
关键词：k-means algorithm; Euclidean distance function; Manhattan distance function; weka tool; clustering; time complexity.