Multirelation Association Rule Mining on Datasets of the Web of Data

Oliveira, Felipe Alves de; Costa, Raquel L.; Goldschmidt, Ronaldo R.; Cavalcanti, Maria Cláudia

doi:10.1145/3330204.3330271

Cited by 2 publications

(4 citation statements)

References 8 publications

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“…The association rule is a machine learning method based on rules, and it is used to find the implicit relationship (A ⇒ B) between transactions from a sample. The method can find the relationship between basket data and other data types, including medical diagnosis [54], web data [55], risk assessment [56], and so on.…”

Section: ) Pruning the Svm-latticementioning

confidence: 99%

SVM-Lattice: A Recognition and Evaluation Frame for Double-Peaked Profiles

Yang

Cai

et al. 2020

IEEE Access

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In big data era, the special data with rare characteristics may be of great significations. However, it is very difficult to automatically search these samples from the massive and high-dimensional datasets and systematically evaluate them. The DoPS, our previous work [1], provided a search method of rare spectra with double-peaked profiles from massive and high-dimensional data of LAMOST survey. The identification of the results is mainly depended on visually inspection by astronomers. In this paper, as a follow-up study, a new lattice structure named SVM-Lattice is designed based on SVM(Support Vector Machine) and FCL(Formal Concept Lattice) and particularly applied in the recognition and evaluation of rare spectra with double-peaked profiles. First, each node in the SVM-Lattice structure contains two components: the intents are defined by the support vectors trained by the spectral samples with the specific characteristics, and the relevant extents are all the positive samples classified by the support vectors. The hyperplanes can be extracted from every lattice node and used as classifiers to search targets by categories. A generalization and specialization relationship is expressed between the layers, and higher layers indicate higher confidence of targets. Then, including a SVM-Lattice building algorithm, a pruning algorithm based on association rules, and an evaluation algorithm, the supporting algorithms are provided and analysed. Finally, for the recognition and evaluation of spectra with double-peaked profiles, several data sets from LAMOST survey are used as experimental dataset. The results exhibit good consistency with traditional methods, more detailed and accurate evaluations of classification results, and higher searching efficiency than other similar methods. INDEX TERMS SVM-Lattice, double-peaked profiles, support vector machine, formal concept lattice.

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Section: ) Pruning the Svm-latticementioning

confidence: 99%

SVM-Lattice: A Recognition and Evaluation Frame for Double-Peaked Profiles

Yang

Cai

et al. 2020

IEEE Access

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“…O fortalecimento da Web de Dados através de iniciativas como o dataset DBpedia, tem permitido a utilização de mecanismos computacionais inteligentes para exploração desses dados na busca por conhecimento em diferentesáreas de aplicação [de Oliveira et al 2019]. Nesse contexto, técnicas de descoberta de conhecimento em bases de dados (do inglês, Knowledge-Discovery in Databases -KDD) podem ser empregadas.…”

Section: Mineração De Regras De Associaçãounclassified

“…O pseudo-código do algoritmo MRAR+ pode ser encontrado em [de Oliveira et al 2019]. Vale notar que, embora o algoritmo especificado seja genérico e permita que quaisquer datasets externos sejam analisados, somente os recursos externos que dão suporte a alguma regra inicialmente encontrada sobre o dataset alvo, serão investigados, reduzindo, dessa forma, o número de datasets a investigar.…”

Section: Solução Propostaunclassified

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Minerando Regras de Associação de Multirrelação na Web de Dados

Oliveira

Villote

Costa

et al. 2020

iSys

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A Web de Dados é uma relevante e crescente fonte de dados que contém informações distribuídas em diferentes datasets interligados. A maioria dos algoritmos de mineração de dados foi projetada para analisar um único dataset por vez, e, consequentemente, não consegue explorar as conexões entre datasets da Web de Dados. Para suprir essa lacuna, este trabalho propõe o MRAR+, um algoritmo de mineração de grafos que busca por regras de associação multirrelação a fim de identificar conhecimentos novos que envolvam recursos de múltiplos datasets da Web de Dados. O MRAR+ foi aplicado com sucesso em dois experimentos e produziu regras novas e úteis para os usuários, ilustrando a sua viabilidade para minerar diferentes datasets interligados na Web de Dados.

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Multirelation Association Rule Mining on Datasets of the Web of Data

Cited by 2 publications

References 8 publications

SVM-Lattice: A Recognition and Evaluation Frame for Double-Peaked Profiles

SVM-Lattice: A Recognition and Evaluation Frame for Double-Peaked Profiles

Minerando Regras de Associação de Multirrelação na Web de Dados

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