Correlation n-star Graphs
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Dátum
2026
Autori
Názov časopisu
ISSN časopisu
Názov zväzku
Vydavateľ
IPSI : Belehrad
ISBN
ISSN
1820-4503
Abstrakt
When working with correlation analysis and the visualization of its results and processes, the systematic identification of correlation classes is often overlooked. Such classes can be described as sets of attributes of the studied dataset that share similar correlation patterns − like the strength and direction of the relationship measured between a pair of attributes − and can be used as a method of identification of significant and insignificant correlations while introducing an attribute hierarchy for automated predictive analysis. This work proposes correlation n-star graphs, a graph-based visualization model designed to support automatic identification of these classes in multidimensional datasets. The work focuses on the design of the visualization model, a Python implementation of the proposed concept, and an experimental evaluation on three benchmark datasets assessing both qualitative and quantitative aspects of the visualization and correlation classification.
Popis
In: IPSI transactions on internet research : multi-, inter-, and trans-disciplinary issues in computer science and engineering. Belehrad : IPSI, 2026. ISSN 1820-4503. Vol. 22, no. 2 (2026), pp. 15-33.
Kľúčové slová
korelačné štruktúry, correlation structures, korelačné efekty, vizualizácia, visualization, dátové analýzy
Výstup z projektu
UGA UMB UGA-14-PDS-2025 Korelačné štruktúry a vizuálna analýza vzorov v mnohorozmerných datasetoch
Citácia
Práva a licenčné podmienky
CC BY-NC-ND Creative Commons Attribution-NonCommercial-NoDerivatives 4.0. International
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess