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A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data

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  • Ali Seyed Shirkhorshidi
  • Saeed Aghabozorgi
  • Teh Ying Wah

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.

Suggested Citation

  • Ali Seyed Shirkhorshidi & Saeed Aghabozorgi & Teh Ying Wah, 2015. "A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-20, December.
    • Handle: RePEc:plo:pone00:0144059
    DOI: 10.1371/journal.pone.0144059
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    1. Ciprian Ionel Turturean & Ciprian Chirilă & Viorica Chirilă, 2022. "The Convergence in the Sustainability of the Economies of the European Union Countries between 2006 and 2016," Sustainability, MDPI, vol. 14(16), pages 1-34, August.
    2. Chávez Bustamante, Felipe O. G. & Mondaca-Marino, Cristian & Rojas-Mora, Julio, 2018. "Dinámicas laborales regionales y su relevancia en el agregado nacional: Una aplicación de Clusterización de Series Temporales para Chile/Regional Labor Dynamics and their Relevance in the National Agg," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 36, pages 961-978, Septiembr.
    3. Brian Stacey, 2017. "A Standardized Treatment of Binary Similarity Measures with an Introduction to k-Vector Percentage Normalized Similarity," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 6(1), pages 1-3.
    4. Oliver Reader, M. & Eppinga, Maarten B. & de Boer, Hugo J. & Petchey, Owen L. & Santos, Maria J., 2024. "Consistent ecosystem service bundles emerge across global mountain, island and delta systems," Ecosystem Services, Elsevier, vol. 66(C).
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    6. Babucea Ana-Gabriela & Rabontu Cecilia-Irina, 2020. "The State Of Adopting Crm Software-Solutions As Part Of The Enterprises’ Internal Processes Integration – A Cluster Analysis At The Level Of The Eu-Member States Just Prior To The Covid-19 Pandemic," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 5, pages 115-125, October.
    7. Giuseppe Orlando & Michele Bufalo, 2021. "Interest rates forecasting: Between Hull and White and the CIR#—How to make a single‐factor model work," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1566-1580, December.
    8. Khaleghikarahrodi, Mehrsa & Macht, Gretchen A., 2023. "Patterns, no patterns, that is the question: Quantifying users’ electric vehicle charging," Transport Policy, Elsevier, vol. 141(C), pages 291-304.
    9. Sergey Dzuba & Denis Krylov, 2021. "Cluster Analysis of Financial Strategies of Companies," Mathematics, MDPI, vol. 9(24), pages 1-21, December.
    10. Payne, Scott & Fuller, Edgar & Spirou, George & Zhang, Cun-Quan, 2022. "Automatic Quasi-Clique Merger Algorithm — A hierarchical clustering based on subgraph-density," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    11. Michael H. Senteney & David L. Stowe & John D. Stowe, 2020. "Financial statement change and equity risk," Review of Financial Economics, John Wiley & Sons, vol. 38(1), pages 63-75, January.
    12. Mahdi Massahi & Masoud Mahootchi & Alireza Arshadi Khamseh, 2020. "Development of an efficient cluster-based portfolio optimization model under realistic market conditions," Empirical Economics, Springer, vol. 59(5), pages 2423-2442, November.

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