Title: Sorting paired points: a dissimilarity measure based on sorting of series

Authors: Wallace Anacleto Pinheiro; Ricardo Q.A. Fernandes; Ana Bárbara Sapienza Pinheiro

Addresses: Systems Development Center, Brazilian Army, Brasília, DF, Brazil ' Systems Development Center, Brazilian Army, Brasília, DF, Brazil ' Department of Tropical Medicine, Brazilian University, Brasília, DF, Brazil

Abstract: We propose a new dissimilarity measure, sorting different time series and measuring their absolute and relative degree of disorganisation. This work compares this strategy with the state-of-the-art of dissimilarities or similarities measures, such as DTW, maximal information coefficient (MIC) and complexity-invariant distance (CID). Two clustering algorithms, one deterministic and one non-deterministic, K-means and hierarchical, allow us to analyse their results. To infer the accuracy, we use two different indexes, maximal HITS, and adjusted Rand index. The results of the experiments, over 128 different datasets, demonstrate that the proposed approach provides more accurate results for different domains using the proposed metrics.

Keywords: clustering; similarity; time series; entropy; sorting.

DOI: 10.1504/IJDMMM.2025.144620

International Journal of Data Mining, Modelling and Management, 2025 Vol.17 No.1, pp.1 - 25

Received: 05 Jan 2024
Accepted: 27 Apr 2024
Published online: 25 Feb 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article