title: A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data.
abstract: Similarity or distance measures are core components used by distance-based clustering algorithms to cluster similar data points into the same clusters, while dissimilar or distant data points are placed into different clusters. The performance of similarity measures is mostly addressed in two or three-dimensional spaces, beyond which, to the best of our knowledge, there is no empirical study that has revealed the behavior of similarity measures when dealing with high-dimensional datasets. To fill this gap, a technical framework is proposed in this study to analyze, compare and benchmark the influence of different similarity measures on the results of distance-based clustering algorithms. For reproducibility purposes, fifteen publicly available datasets were used for this study, and consequently, future distance measures can be evaluated and compared with the results of the measures discussed in this work. These datasets were classified as low and high-dimensional categories to study the performance of each measure against each category. This research should help the research community to identify suitable distance measures for datasets and also to facilitate a comparison and evaluation of the newly proposed similarity or distance measures with traditional ones.
authors.affiliation: Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia.
authors.forename: Ali Seyed
authors.initials: AS
authors.lastname: Shirkhorshidi
authors.affiliation: IBM Analytics, Platform, Emerging Technologies, IBM Canada Ltd., Markham, Ontario L6F 1C7, Canada.
authors.forename: Saeed
authors.initials: S
authors.lastname: Aghabozorgi
authors.affiliation: Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia.
authors.forename: Teh Ying
authors.initials: TY
authors.lastname: Wah
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D019985:Benchmarking; D016000:Cluster Analysis; D057225:Data Mining / Q000706:statistics & numerical data*; D066264:Datasets as Topic