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
