A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data
By Ali Seyed Shirkhorshidi, Saeed Reza Aghabozorgi, Teh Ying Wah, Andrew R. Dalby. PLoS ONE, 2015
380 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/9604ecd2cc5c30c8ea5fd205cea23fae49e88f39
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In Artificial Intelligence
Citations in the corpus
- Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review (2017, positive): “As given in Table 11, the p -values of accuracy results are less than the significance level (0.05) eight times, here we can reject the null hypothesis and conclude that there is a significant difference in the performance of HasD compared with ED, CanD, CosD, ClaD, SCSD, WIAD, CorD, and DivD, and sin”