NARFO Algorithm: Mining Non-redundant and Generalized Association Rules Based on Fuzzy Ontologies

Miani, Rafael Garcia Leonel; Yaguinuma, Cristiane A.; Santos, Marilde Terezinha Prado; Biajiz, Mauro

doi:10.1007/978-3-642-01347-8_35

Cited by 16 publications

(14 citation statements)

References 7 publications

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“…• minSim, which is the minimum similarity used in the reasoner inferences (Miani et al, 2009); • side, which represents the side of generalization;…”

Section: Main Ideasmentioning

confidence: 99%

“…The similarities are represented in the leaves of ontology. Relations with similarity degree value greater than or equal to the user-provide minSim (Miani et al, 2009) can be show in the rules generated, increasing the semantic enrichment of the same. The generalization is made through a sub-algorithm that uses a methodology of grouping and replacement in the rules.…”

Section: Main Ideasmentioning

confidence: 99%

“…Besides, some works, like (Miani et al, 2009) and (Escovar et al, 2006), explore the semantic enrichment through similarity relations. However, these works do not consider that the degree of a similarity relation, between two or more elements, it is also related to the point of view or to the context analysed.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, some works, like (Escovar et al, 2006) and (Miani et al, 2009) are directed to the semantic of the data mined. They use ontologies for extract associations of similarity existing between items of the database.…”

Section: Introductionmentioning

confidence: 99%

“…These relations are represented in the leaves of ontology, but the specialization/generalization degrees are constant 1, like crisp ontologies. The work (Miani et al, 2009) is an extension of (Escovar et al, 2006), and the main differences are the introduction of a redundancy treatment and a step of generalizing non-frequent itemsets. However, both algorithms are limited, since generalizes at only one level of ontology (leaf nodes to parents).…”

Section: Introductionmentioning

confidence: 99%

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Mining Generalized Association Rules using Fuzzy Ontologies with Context-based Similarity

Ayres

Santos

2012

Proceedings of the 14th International Conference on Enterprise Information Systems

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In crisp contexts taxonomies are used in different steps of the mining process. When the objective is the generalization they are used, manly, in the pre-processing or post-processing stages. On the other hand, in fuzzy contexts, fuzzy taxonomies are used, mainly, in the pre-processing step, during the generation of extended transactions. A great problem of such transactions is related to the generation of huge amount of candidates and rules. Beyond that, the inclusion of ancestors in the same ends up generating problems of redundancy. Besides, it is possible to see that many works have directed efforts for the question of mining fuzzy rules, exploring linguistic terms, but few approaches have proposed new steps of the mining process. In this sense, this paper propose the Context FOntGAR algorithm, a new algorithm for mining generalized association rules under all levels of fuzzy ontologies composed by specialization/generalization degrees varying in the interval [0,1]. In order to obtain more semantic enrichment, the rules may be composed by similarity relations, which are represented at the fuzzy ontologies in different contexts. In this work the generalization is done during the post-processing step. Other relevant points are the specification of a generalization approach; including a grouping rules treatment, and an efficient way of calculating both support and confidence of generalized rules during this step.

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“…• minSim, which is the minimum similarity used in the reasoner inferences (Miani et al, 2009); • side, which represents the side of generalization;…”

Section: Main Ideasmentioning

confidence: 99%

Section: Main Ideasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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