Frequent Itemset Mining from Databases Including One Evidential Attribute

Tobji, Mohamed Anis Bach; Yaghlane, Boutheina Ben; Mellouli, Khaled

doi:10.1007/978-3-540-87993-0_4

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…This model opens up the way for further researches on querying possible worlds' model to validate existing querying methods based on the compact form with the aim of proving the strong representation system relative to evidential databases.…”

Section: Discussionmentioning

confidence: 99%

Modeling evidential databases as possible worlds

Bousnina

Tobji

Chebbah

et al. 2018

Int. J. Intell. Syst.

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This paper is about modeling a possible worlds' form of imperfect databases, where imperfection is handled with the evidence theory. The possible worlds' form is already developed for other types of imperfect databases such as probabilistic and possibilistic databases. Such a form is essential to prove that a compact form querying method is a strong representation system. For the same aim, we propose in this article an extended model of possible worlds's form treating the tuple‐level uncertainty in addition to the attribute‐level uncertainty.

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Section: Discussionmentioning

confidence: 99%

Modeling evidential databases as possible worlds

Bousnina

Tobji

Chebbah

et al. 2018

Int. J. Intell. Syst.

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“…In [8], tests were conducted on synthetic database. Even in [15], the constructed BBA includes only one evidential attributes. In [9], the authors worked on a simplified naval anti-surface warfare scenario.…”

Section: Evidential Database Constructionmentioning

confidence: 99%

Mining Frequent Itemsets in Evidential Database

Samet

Lefèvre

Yahia

2014

Advances in Intelligent Systems and Computing

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Mining frequent patterns is widely used to discover knowledge from a database. It was originally applied on Market Basket Analysis (MBA) problem which represents the Boolean databases. In those databases, only the existence of an article (item) in a transaction is defined. However, in real-world application, the gathered information generally suffer from imperfections. In fact, a piece of information may contain two types of imperfection: imprecision and uncertainty. Recently, a new database representing and integrating those two types of imperfection were introduced: Evidential Database. Only few works have tackled those databases from a data mining point of view. In this work, we aim to discuss evidential itemset's support. We improve the complexity of state of art methods for support's estimation. We also introduce a new support measure gathering fastness and precision. The proposed methods are tested on several constructed evidential databases showing performance improvement.

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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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Frequent Itemset Mining from Databases Including One Evidential Attribute

Cited by 5 publications

References 15 publications

Modeling evidential databases as possible worlds

Modeling evidential databases as possible worlds

Mining Frequent Itemsets in Evidential Database

Selecting Relevant Association Rules From Imperfect Data

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