In multi criteria Decision Making, the decision maker wants to find the best alternative among a set of alternatives in order to satisfy a set of criteria. Traditionally, decision making models are based on crisp data. The shortcoming of these data in capturing the reality and lack of information persuaded researchers to develop decision making methods with uncertain data. In this paper, the EL...

There has been much interest in answering top-k queries on probabilistic data in various applications such as market analysis, personalised services, and decision making. In relation to probabilistic data, the most common problem in answering top-k queries is selecting the semantics of results according to their scores and top-k probabilities. In this paper, we propose a novel top-k best probab...

In a lot of operational situations, we have to deal with uncertain and inaccurate information. The theory of belief functions is a mathematical framework useful to handle this kind of imperfection. However, in most of the cases, uncertain data are modeled with a distribution of probability. We present in this paper different principles to induce belief functions from probabilities. Hence, we de...

Image understanding applications are often tainted with a high degree of complexity, uncertainty, and imprecision. The large amount of data makes it necessary to select the most useful information. The active fusion system proposed in this paper is able to eeectively select information sources, to control the acquisition process, to select processing strategies, to integrate results, and to dra...

In this paper, we introduce the notion of τ -skyline as an alternative representation for uncertain skylines. Given a parameter τ ∈ [0,1] and a set P of n uncertain points in the plane, where each uncertain point Pi is described by a discrete probability distribution defined over k locations, the τ -skyline region of P is the set of points q in the plane such that the probability of any Pi domi...

In Analytic Hierarchy Process (AHP) structured hierarchically as several criteria and alternatives, the priority of an alternative is obtained by using the pairwise comparisons based on decision maker’s intuition. Thus, the given comparisons are uncertain and inconsistent each other. We use the interval approach for obtaining interval evaluations which are suitable for handling uncertain data. ...

In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy ...