Matches in SemOpenAlex for { <https://semopenalex.org/work/W20530366> ?p ?o ?g. }
Showing items 1 to 67 of
67
with 100 items per page.
- W20530366 endingPage "511" @default.
- W20530366 startingPage "509" @default.
- W20530366 abstract "This paper presents a numerical association rule extraction method that is based on original quality measures which evaluate to what extent a numerical classification model behaves as a natural symbolic classifier such as a Galois lattice. The proposed method copes with the usual problems of the symbolic association rule extraction method that are computation time and rule selection. Introduction Symbolic association rule extraction models [1] suffer of very serious limitations. Rule generation is a highly timeconsuming process that generates a huge number of rules, including a large ratio of redundant rules. Hence, this prohibits any kind of rule computation and selection as soon as data are numerous and they are represented by very high-dimensional description space. This latter situation is very often encountered with documentary data. In this paper we propose a new approach for knowledge extraction that consists in using a MultiGAS model as a front-end for unsupervised extraction of association rules. In our approach we exploit both the generalization and the intercommunication mechanisms of the model. We also make use of our original recall and precision measures that derive from the Galois lattice theory and from Information Retrieval (IR) domains. Basic principles The MultiGAS model is a neural network model that represents a viewpoint-oriented extension of the Neural Gas model. Its main principle is to be constituted by several gases that have been generated from the same data. Each gas is itself issued from a specific data description subspace (i.e. viewpoint). The relation between gases is established through the use of two main mechanisms: the inter-gas communication mechanism and the generalization mechanism. A detailed description of the model is given in [2]. Copyright © 2007, American Association for Artificial Intelligence (www.aaai.org). All rights reserved. The classical evaluation measures for the quality of classification are based on the intra-class inertia and the inter-class inertia (see [3]). These measures are often strongly biased because they depend both on the preprocessing and on the classification methods. Therefore, we have proposed to derive from the Galois lattice and Information Retrieval (IR) domains two new quality measures, Recall and Precision. The Precision and Recall measures are based on the properties of class members [3]. The Precision criterion measures in which proportion the content of the classes generated by a classification method is homogeneous. The greater the Precision, the nearer the intensions of the data belonging to the same classes will be one with respect to the other, and consequently, the more homogenous will be the classes. In a complementary way, the Recall criterion measures the exhaustiveness of the content of said classes, evaluating to what extent single properties are associated with single classes. The Recall criterion should be considered as a specific application of the statistical concept of sensitivity (i.e. true positive rate) to class properties. The Recall (Rec) and Precision (Prec) measures for a given property p of the class c are expressed as:" @default.
- W20530366 created "2016-06-24" @default.
- W20530366 creator A5081537284 @default.
- W20530366 creator A5086703296 @default.
- W20530366 date "2007-01-01" @default.
- W20530366 modified "2023-09-26" @default.
- W20530366 title "An Extended Neural Gas Model for Efficient Data Mining Tasks." @default.
- W20530366 cites W1553696291 @default.
- W20530366 cites W1574179123 @default.
- W20530366 cites W2106372073 @default.
- W20530366 cites W2107712274 @default.
- W20530366 hasPublicationYear "2007" @default.
- W20530366 type Work @default.
- W20530366 sameAs 20530366 @default.
- W20530366 citedByCount "0" @default.
- W20530366 crossrefType "proceedings-article" @default.
- W20530366 hasAuthorship W20530366A5081537284 @default.
- W20530366 hasAuthorship W20530366A5086703296 @default.
- W20530366 hasConcept C11413529 @default.
- W20530366 hasConcept C119857082 @default.
- W20530366 hasConcept C124101348 @default.
- W20530366 hasConcept C154945302 @default.
- W20530366 hasConcept C193524817 @default.
- W20530366 hasConcept C41008148 @default.
- W20530366 hasConcept C45374587 @default.
- W20530366 hasConcept C50644808 @default.
- W20530366 hasConcept C80444323 @default.
- W20530366 hasConcept C95623464 @default.
- W20530366 hasConceptScore W20530366C11413529 @default.
- W20530366 hasConceptScore W20530366C119857082 @default.
- W20530366 hasConceptScore W20530366C124101348 @default.
- W20530366 hasConceptScore W20530366C154945302 @default.
- W20530366 hasConceptScore W20530366C193524817 @default.
- W20530366 hasConceptScore W20530366C41008148 @default.
- W20530366 hasConceptScore W20530366C45374587 @default.
- W20530366 hasConceptScore W20530366C50644808 @default.
- W20530366 hasConceptScore W20530366C80444323 @default.
- W20530366 hasConceptScore W20530366C95623464 @default.
- W20530366 hasLocation W205303661 @default.
- W20530366 hasOpenAccess W20530366 @default.
- W20530366 hasPrimaryLocation W205303661 @default.
- W20530366 hasRelatedWork W163275269 @default.
- W20530366 hasRelatedWork W2034232946 @default.
- W20530366 hasRelatedWork W2104677983 @default.
- W20530366 hasRelatedWork W2187307722 @default.
- W20530366 hasRelatedWork W2484776493 @default.
- W20530366 hasRelatedWork W2785267325 @default.
- W20530366 hasRelatedWork W2886963752 @default.
- W20530366 hasRelatedWork W2892867475 @default.
- W20530366 hasRelatedWork W2895954256 @default.
- W20530366 hasRelatedWork W2909055072 @default.
- W20530366 hasRelatedWork W2953192166 @default.
- W20530366 hasRelatedWork W2988530446 @default.
- W20530366 hasRelatedWork W3009145053 @default.
- W20530366 hasRelatedWork W3081613211 @default.
- W20530366 hasRelatedWork W3122127382 @default.
- W20530366 hasRelatedWork W3153986382 @default.
- W20530366 hasRelatedWork W3157579670 @default.
- W20530366 hasRelatedWork W3175175767 @default.
- W20530366 hasRelatedWork W66576182 @default.
- W20530366 hasRelatedWork W96044246 @default.
- W20530366 isParatext "false" @default.
- W20530366 isRetracted "false" @default.
- W20530366 magId "20530366" @default.
- W20530366 workType "article" @default.