In recent years, due to the wide applications of uncertain data (e.g., noisy data), uncertain frequent itemsets (UFI) mining over uncertain databases has attracted much attention, which differs from the corresponding deterministic problem from the generalized definition and resolutions. As the most costly task in association rule mining process, it has been shown that outsourcing this task to a...

This paper presents a method for solving the multicriteria group decision making problem in an uncertain linguistic environment. To ensure an effective aggregation of the uncertain linguistic variables in multicriteria group decision making for adequately modeling the uncertainty and imprecision of the decision making process, an uncertain linguistic Choquet integral operator is proposed. A met...

Uncertainty may be taken to characterize in­ ferences, their conclusions, their premises or all three. Under some treatments of uncer­ tainty, the inference itself is never character­ ized by uncertainty. We explore both the sig­ nificance of uncertainty in the premises and in the conclusion of an argument that in­ volves uncertainty. We argue that for un­ certainty to characterize the conclusi...

*Correspondence: [email protected] 1School of Science, Nanjing University of Science and Technology, 210094 Nanjing, China Full list of author information is available at the end of the article Abstract Based on the concept of uncertain process, uncertain linear systems with multiple input delays are considered in this paper. An optimal problem for such systems is introduced and solved by usin...

Uncertainty is an intrinsic feature of automatic and semiautomatic data integration processes. Although many solutions have been proposed to reduce uncertainty, if we do not explicitly represent and keep it up to the end of the integration process we risk to lose relevant information, and to produce misleading results. Models for uncertain data can then be used to represent integrated data sour...

We introduce an adaptive algorithm to estimate the uncertain parameter of a stochastic optimization problem. The procedure estimates the one-step-ahead means, variances and covariances of a random process in a distribution-free and multidimensional framework when these means, variances and covariances are slowly varying on a given past interval. The quality of the approximate problem obtained w...

In this paper we argue that the development of knowledgebased systems built to work in partially uncertain domains bene t from the use of di erent conceptualisations for certain and uncertain parts of the knowledge. We present conceptualisations that have proven to be useful, namely the KADS model of expertise and a causal model of uncertainty that re ects well known approaches to uncertain rea...

In the domain of ubiquitous computing, the ability to identify the occurrence of situations is a core function of being 'contextaware'. Given the uncertain nature of sensor information and inference rules, reasoning techniques that cater for uncertainty hold promise for enabling the inference process. In our work, we apply the Dempster Shafer theory of evidence to determine situation occurrence...

This paper is concerned with the full-order H∞ filtering problem for uncertain differential linear repetitive processes (LRPs). uncertain differential linear repetitive process (LRP) assumed to be stable along the pass, our attention is focused on the design of a full-order filter, which guarantees the filtering error process to be stable along the pass, and minimizes an upper bound for theH∞ n...