Computational Experiments to Analyze the Effectiveness of the Algorithm for Decomposing a Formal Context into Fragments

The article presents the results of computational experiments. The following is evaluated: the effectiveness of the algorithm for fragmentation and defragmentation of the formal context with a single decomposition of the context; the number of fragments formed depending on the density of the original formal context; the number of possible iterations of the fragmentation process; the number of fragments and the running time of the algorithm for the context depending on the threshold value σ for the fragments being formed.

Authors

Institutions

Publication Details

Published
2025-04-01
DOI
https://doi.org/10.63550/iceip.2025.1.1.141
Primary Topic
Mathematics, Computing, and Information Processing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Computational Experiments to Analyze the Effectiveness of the Algorithm for Decomposing a Formal Context into Fragments

Choduraa M. Mongush, Sergey Ondar, D.B. Bayyr, C.K. Oorzhak
Mathematics, Computing, and Information Processing
article

Computational Experiments to Analyze the Effectiveness of the Algorithm for Decomposing a Formal Context into Fragments

Choduraa M. Mongush, Sergey Ondar, D.B. Bayyr, C.K. Oorzhak
article en

Abstract

The article presents the results of computational experiments. The following is evaluated: the effectiveness of the algorithm for fragmentation and defragmentation of the formal context with a single decomposition of the context; the number of fragments formed depending on the density of the original formal context; the number of possible iterations of the fragmentation process; the number of fragments and the running time of the algorithm for the context depending on the threshold value σ for the fragments being formed.

Tuvan State University (RU)
Openalex Percentile: Top 89%
Mathematics, Computing, and Information Processing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.