slugStudy of Euclidean and Manhattan Distance Metrics using Simple K-Means Clustering
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 5
Abstract
Clustering is the task of assigning a set of objects into groups called clusters in which objects in the same cluster are more similar to each other than to those in other clusters. Generally clustering is used to find out the similar, dissimilar and outlier items from the databases. The main idea behind the clustering is the distance between the data items. The work carried out in this paper is based on the study of two popular distance metrics viz. Euclidean and Manhattan. A series of experiments has been performed to validate the study. We use two real and one synthetic datasets on simple K-Means clustering. The theoretical analysis and experimental results show that the Euclidean method outperforms Manhattan method in terms of number of iterations performed during centroid calculation.
Authors and Affiliations
Deepak Sinwar, Rahul Kaushik
Keywords
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- EP ID EP18115
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