Decomposition of Classification Context as a Tool for Big Data Management

Xenia Naidenova

2021

Abstract

The paper considers a problem of generating all classification Good Maximally Redundant Tests (GMRTs) as the set of all maximal elements of the formal concept lattice generated over a classification context. The number of concepts is exponential in the size of input context and decomposing contexts is one of the methods to decrease the computational complexity of inferring GMRTs. Three kinds of sub-contexts are defined: attributive, object and object-attributive ones. The rules of reducing sub-contexts are given. The properties of the sub-contexts are analysed related to the fact that the set of all GMRTs in a classification context is a completely separating system. Some strategies are considered for choosing sub-contexts based on the definition of essential objects and attribute values. The rules of the decomposition proposed imply constructing some incremental procedures to construct GMRTs. Two methods of pre-processing the formal contexts greatly decreasing the computational complexity of inferring GMRTs are proposed: finding the number of subtasks to be solved (the number of essential values) and the initial content of the set of GMRTs. Some unsolved problems difficult for analytical investigations have been formulated. The decomposition proposed can be fruitful in processing big data based on machine learning algorithm.

Paper Citation

in Harvard Style

Naidenova X. (2021). Decomposition of Classification Context as a Tool for Big Data Management. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML, ISBN 978-989-758-559-3, pages 295-300. DOI: 10.5220/0010732800003101

in Bibtex Style

@conference{bml21,
author={Xenia Naidenova},
title={Decomposition of Classification Context as a Tool for Big Data Management},
booktitle={Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,},
year={2021},
pages={295-300},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010732800003101},
isbn={978-989-758-559-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning - Volume 1: BML,
TI - Decomposition of Classification Context as a Tool for Big Data Management
SN - 978-989-758-559-3
AU - Naidenova X.
PY - 2021
SP - 295
EP - 300
DO - 10.5220/0010732800003101