A novel characterisation-based algorithm to discover new knowledge from classification datasets without use of support. (1st March 2018)

• Record Type: Journal Article
• Authors: Lazcorreta Puigmartí, Enrique; Botella, Federico; Fernández-Caballero, Antonio
• Is Part Of: Expert systems with applications. Volume 93(2018)
• Page Start: 223
• Page End: 231
• Publication Date: 2018-03-01
• Subjects: Classification dataset -- Catalogue -- Association rules mining -- Classification association rules mining; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.10.029
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We define a catalogue as a classification dataset without duplicate instances. Every classification dataset has inside a collection of robust catalogues. Both datasets and their derived catalogues contain the same association rules. Robust catalogues, i.e. catalogues without uncertainty, have interesting properties. Our algorithm gets efficiently all robust catalogues inside a classification dataset. Abstract: This paper introduces a novel proposal to discover the best associative classification rules through studying the influence of the attributes used in robust catalogues. Notice that a catalogue is defined as a dataset free of duplicate records. Moreover, a robust catalogue is obtained when incomplete records and those with uncertainty are eliminated from a catalogue. Therefore, a robust catalogue is a collection of association rules with 100% confidence and unitary support. In this paper we demonstrate that robust catalogues contain the same association rules as the datasets from which they were obtained, but can be managed in memory without eliminating any data from the analysis. In fact, the experiments performed show that all robust catalogues contained in a classification dataset are easily obtained, providing millions of associative classification rules with 100% confidence to the expert researcher in data mining.