Elements About Exploratory, Knowledge-Based, Hybrid, and Explainable Knowledge Discovery

Miguel Couceiro 1 Amedeo Napoli 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Knowledge Discovery in Databases (KDD) and especially pattern mining can be interpreted along several dimensions, namely data, knowledge, problem-solving and interactivity. These dimensions are not disconnected and have a direct impact on the quality, applicability, and efficiency of KDD. Accordingly, we discuss some objectives of KDD based on these dimensions, namely exploration, knowledge orientation, hybridization, and explanation. The data space and the pattern space can be explored in several ways, depending on specific evaluation functions and heuristics, possibly related to domain knowledge. Furthermore, numerical data are complex and supervised numerical machine learning methods are usually the best candidates for efficiently mining such data. However, the work and output of numerical methods are most of the time hard to understand, while symbolic methods are usually more intelligible. This calls for hybridization, combining numerical and symbolic mining methods to improve the applicability and interpretability of KDD. Moreover, suitable explanations about the operating models and possible subsequent decisions should complete KDD, and this is far from being the case at the moment. For illustrating these dimensions and objectives, we analyze a concrete case about the mining of biological data, where we characterize these dimensions and their connections. We also discuss dimensions and objectives in the framework of Formal Concept Analysis and we draw some perspectives for future research.
Complete list of metadatas

Cited literature [58 references]  Display  Hide  Download

https://hal.inria.fr/hal-02195480
Contributor : Amedeo Napoli <>
Submitted on : Tuesday, September 10, 2019 - 3:48:18 PM
Last modification on : Tuesday, September 10, 2019 - 5:21:15 PM

File

mc+an-icfca19.pdf
Files produced by the author(s)

Identifiers

Données associées

Collections

Citation

Miguel Couceiro, Amedeo Napoli. Elements About Exploratory, Knowledge-Based, Hybrid, and Explainable Knowledge Discovery. ICFCA 2019 - 15th International Conference on Formal Concept Analysis, Jun 2019, Frankfurt, Germany. pp.3-16, ⟨10.1007/978-3-030-21462-3_1⟩. ⟨hal-02195480⟩

Share

Metrics

Record views

36

Files downloads

100