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

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Executive Summary

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.

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