Comparative Analysis & Evaluation of Euclidean Distance Function and Manhattan Distance Function Using K-Means Algorithm

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.

Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE) Topic: Software Date Added: Jul 2012 Format: PDF

Find By Topic