Open AccessPerformance evaluation of similarity measures for K-means clustering algorithm
Author(s) -
Dilawar Usman,
Sadiq Sani
Publication year - 2021
Publication title -
bayero journal of pure and applied sciences
Language(s) - English
Resource type - Journals
ISSN - 2006-6996
DOI - 10.4314/bajopas.v12i2.21
Subject(s) - cluster analysis , similarity (geometry) , euclidean distance , k medians clustering , data mining , computer science , single linkage clustering , pattern recognition (psychology) , correlation clustering , clustering high dimensional data , cure data clustering algorithm , mathematics , fuzzy clustering , artificial intelligence , algorithm , image (mathematics)
Clustering is a useful technique that organizes a large quantity of unordered datasets into a small number of meaningful and coherent clusters. Every clustering method is based on the index of similarity or dissimilarity between data points. However, the true intrinsic structure of the data could be correctly described by the similarity formula defined and embedded in the clustering criterion function. This paper uses squared Euclidean distance and Manhattan distance to investigates the best method for measuring similarity between data objects in sparse and high-dimensional domain which is fast, capable of providing high quality clustering result and consistent. The performances of these two methods were reported with simulated high dimensional datasets.