TY  - JOUR
AU  - Mihailović, Dragutin T.
AU  - Nikolić-Đorić, Emilija
AU  - Malinović-Milićević, Slavica
AU  - Singh, Vijay P.
AU  - Mihailović, Anja
AU  - Stošić, Tatjana
AU  - Stošić, Borko
AU  - Drešković, Nusret
PY  - 2019
UR  - https://dais.sanu.ac.rs/123456789/12914
AB  - The purpose of this paper was to choose an appropriate information dissimilarity measure for hierarchical clustering of daily streamflow discharge data, from twelve gauging stations on the Brazos River in Texas (USA), for the period 1989–2016. For that purpose, we selected and compared the average-linkage clustering hierarchical algorithm based on the compression-based dissimilarity
measure (NCD), permutation distribution dissimilarity measure (PDDM), and Kolmogorov distance (KD). The algorithm was also compared with K-means clustering based on Kolmogorov complexity (KC), the highest value of Kolmogorov complexity spectrum (KCM), and the largest Lyapunov exponent (LLE). Using a dissimilarity matrix based on NCD, PDDM, and KD for daily streamflow,
the agglomerative average-linkage hierarchical algorithm was applied. The key findings of this study are that: (i) The KD clustering algorithm is the most suitable among others; (ii) ANOVA analysis shows that there exist highly significant differences between mean values of four clusters, confirming that the choice of the number of clusters was suitably done; and (iii) from the clustering we found that the predictability of streamflow data of the Brazos River given by the Lyapunov time (LT), corrected for randomness by Kolmogorov time (KT) in days, lies in the interval from two to five days.
PB  - Switzerland, Basel : MDPI
T2  - Entropy
T1  - The Choice of an Appropriate Information Dissimilarity Measure for Hierarchical Clustering of River Streamflow Time Series, Based on Calculated Lyapunov Exponent and Kolmogorov Measures
SP  - 215
VL  - 21
IS  - 2
DO  - 10.3390/e21020215
UR  - https://hdl.handle.net/21.15107/rcub_dais_12914
ER  - 

@article{
author = "Mihailović, Dragutin T. and Nikolić-Đorić, Emilija and Malinović-Milićević, Slavica and Singh, Vijay P. and Mihailović, Anja and Stošić, Tatjana and Stošić, Borko and Drešković, Nusret",
year = "2019",
abstract = "The purpose of this paper was to choose an appropriate information dissimilarity measure for hierarchical clustering of daily streamflow discharge data, from twelve gauging stations on the Brazos River in Texas (USA), for the period 1989–2016. For that purpose, we selected and compared the average-linkage clustering hierarchical algorithm based on the compression-based dissimilarity
measure (NCD), permutation distribution dissimilarity measure (PDDM), and Kolmogorov distance (KD). The algorithm was also compared with K-means clustering based on Kolmogorov complexity (KC), the highest value of Kolmogorov complexity spectrum (KCM), and the largest Lyapunov exponent (LLE). Using a dissimilarity matrix based on NCD, PDDM, and KD for daily streamflow,
the agglomerative average-linkage hierarchical algorithm was applied. The key findings of this study are that: (i) The KD clustering algorithm is the most suitable among others; (ii) ANOVA analysis shows that there exist highly significant differences between mean values of four clusters, confirming that the choice of the number of clusters was suitably done; and (iii) from the clustering we found that the predictability of streamflow data of the Brazos River given by the Lyapunov time (LT), corrected for randomness by Kolmogorov time (KT) in days, lies in the interval from two to five days.",
publisher = "Switzerland, Basel : MDPI",
journal = "Entropy",
title = "The Choice of an Appropriate Information Dissimilarity Measure for Hierarchical Clustering of River Streamflow Time Series, Based on Calculated Lyapunov Exponent and Kolmogorov Measures",
pages = "215",
volume = "21",
number = "2",
doi = "10.3390/e21020215",
url = "https://hdl.handle.net/21.15107/rcub_dais_12914"
}

Mihailović, D. T., Nikolić-Đorić, E., Malinović-Milićević, S., Singh, V. P., Mihailović, A., Stošić, T., Stošić, B.,& Drešković, N.. (2019). The Choice of an Appropriate Information Dissimilarity Measure for Hierarchical Clustering of River Streamflow Time Series, Based on Calculated Lyapunov Exponent and Kolmogorov Measures. in Entropy
Switzerland, Basel : MDPI., 21(2), 215.
https://doi.org/10.3390/e21020215
https://hdl.handle.net/21.15107/rcub_dais_12914

Mihailović DT, Nikolić-Đorić E, Malinović-Milićević S, Singh VP, Mihailović A, Stošić T, Stošić B, Drešković N. The Choice of an Appropriate Information Dissimilarity Measure for Hierarchical Clustering of River Streamflow Time Series, Based on Calculated Lyapunov Exponent and Kolmogorov Measures. in Entropy. 2019;21(2):215.
doi:10.3390/e21020215
https://hdl.handle.net/21.15107/rcub_dais_12914 .

Mihailović, Dragutin T., Nikolić-Đorić, Emilija, Malinović-Milićević, Slavica, Singh, Vijay P., Mihailović, Anja, Stošić, Tatjana, Stošić, Borko, Drešković, Nusret, "The Choice of an Appropriate Information Dissimilarity Measure for Hierarchical Clustering of River Streamflow Time Series, Based on Calculated Lyapunov Exponent and Kolmogorov Measures" in Entropy, 21, no. 2 (2019):215,
https://doi.org/10.3390/e21020215 .,
https://hdl.handle.net/21.15107/rcub_dais_12914 .