Evidential data mining: precise support and confidence

Samet, Ahmed; Lefèvre, Éric; Yahia, Sadok Ben

doi:10.1007/s10844-016-0396-5

Cited by 16 publications

(3 citation statements)

References 29 publications

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“…Uncertainty theories have also been investigated for imperfect data in ARM using fuzzy logic [14], or using possibility theory [8]. In the case of missing and incomplete data, evidential theory seems the appropriate setting to handle ARM problem [13,19,24,25]. Our approach is adopting this setting.…”

Section: Problem Statement and Related Workmentioning

confidence: 99%

Selecting Relevant Association Rules From Imperfect Data

L’Héritier

Harispe

Imoussaten

et al. 2019

Lecture Notes in Computer Science

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Association Rule Mining (ARM) in the context of imperfect data (e.g. imprecise data) has received little attention so far despite the prevalence of such data in a wide range of real-world applications. In this work, we present an ARM approach that can be used to handle imprecise data and derive imprecise rules. Based on evidence theory and Multiple Criteria Decision Analysis, the proposed approach relies on a selection procedure for identifying the most relevant rules while considering information characterizing their interestingness. The several measures of interestingness defined for comparing the rules as well as the selection procedure are presented. We also show how a priori knowledge about attribute values defined into domain taxonomies can be used to (i) ease the mining process, and to (ii) help identifying relevant rules for a domain of interest. Our approach is illustrated using a concrete simplified case study related to humanitarian projects analysis.

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Section: Problem Statement and Related Workmentioning

confidence: 99%

Selecting Relevant Association Rules From Imperfect Data

L’Héritier

Harispe

Imoussaten

et al. 2019

Lecture Notes in Computer Science

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“…In the following, we compare a classical evidential pattern mining approaches such as EDMA [16] and U-Apriori [6] with the output of OpMiner. To do so, we compare these three algorithms in terms of number of extracted patterns and computational time.…”

Section: Algorithm 1 Opminer Algorithmmentioning

confidence: 99%

Expert Opinion Extraction from a Biomedical Database

Samet

Guyet

Négrevergne

et al. 2017

Lecture Notes in Computer Science

Self Cite

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Abstract. In this paper, we tackle the problem of extracting frequent opinions from uncertain databases. We introduce the foundation of an opinion mining approach with the definition of pattern and support measure. The support measure is derived from the commitment definition. A new algorithm called OpMiner that extracts the set of frequent opinions modelled as a mass functions is detailed. Finally, we apply our approach on a real-world biomedical database that stores opinions of experts to evaluate the reliability level of biomedical data. Performance analysis showed a better quality patterns for our proposed model in comparison with literature-based methods.

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“…Evidential Data mining: Precise Support and Confidence by Ahmed Samet, Eric Lefevre and Sadok Ben Yahia (Samet et al 2016) proposes a new data mining approach to extract frequent patterns with probabilistic reasoning. The retrieved association rules, as well as their respective confidence values, are weighted with respect to their relevance.…”

Section: Efficient Energy-based Embedding Models For Link Prediction mentioning

confidence: 99%

Recent advances in mining patterns from complex data

et al. 2016

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Data mining and knowledge discovery are advanced research fields with numerous algorithms and studies to extract patterns and models from complex data sources like blogs, event or log data, biological data, spatio-temporal data, social networks, mobility data, and sensor data and streams. The works presented in this special issue of the Journal of Intelligent Information Systems should keep the attention of both researchers and practitioners of data mining who are interested in the advances and latest developments in the area of extracting patterns. Behavioral Process Mining for Unstructured Processes by Claudia Diamantini, Laura Genga and Domenico Potena addresses the challenging problem of extracting useful information from the huge volume of events recorded by several of today's enterprise systems

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Evidential data mining: precise support and confidence

Cited by 16 publications

References 29 publications

Selecting Relevant Association Rules From Imperfect Data

Selecting Relevant Association Rules From Imperfect Data

Expert Opinion Extraction from a Biomedical Database

Recent advances in mining patterns from complex data

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