Inconsistency Measurement for Improving Logical Formula Clustering
Authors: Yakoub Salhi
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This study proposes an approach for defining clustering methods that deal with bases of propositional formulas in classical logic... We first use a postulate-based approach for introducing an intuitive framework for formula clustering. Then, in order to characterize interesting clustering forms, we introduce additional properties... Finally, we describe our approach that shows how the inconsistency measures can be involved in improving the task of formula clustering. The main idea consists in using the measures for quantifying the quality of the inconsistent clusters. In this context, we propose further properties that allow characterizing interesting aspects related to the amount of inconsistency. |
| Researcher Affiliation | Academia | Yakoub Salhi CRIL-CNRS, Universit e d Artois, France salhi@cril.fr |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention the release of any open-source code or provide links to a code repository. |
| Open Datasets | No | This is a theoretical paper that focuses on a logical framework, not on empirical evaluation with publicly available datasets. Examples used are illustrative. |
| Dataset Splits | No | As this is a theoretical paper without empirical experiments, there are no dataset splits for training, validation, or testing mentioned. |
| Hardware Specification | No | As this is a theoretical paper without empirical experiments, there are no hardware specifications mentioned. |
| Software Dependencies | No | As this is a theoretical paper without empirical experiments, there are no software dependencies with version numbers mentioned. |
| Experiment Setup | No | As this is a theoretical paper without empirical experiments, there are no specific experimental setup details or hyperparameters provided. |