Study of an Abating Aggregation Operator in Many-Valued Logic

International audienceThis paper considers a parametrised aggregation operator, originally introduced in the formal framework of many-valued logic and in the applicative context of information scoring. It studies this operator, outside this applicative context, looking at specific configurations of interest: highlighting the wide range of its instantiations, from the lower to the upper extreme cases; showing some t-norms it can encode, as specific cases; and also how it allows rich and flexible intermediate behaviours

Fast community structure local uncovering by independent vertex-centred process

This paper addresses the task of community detection and proposes a local approach based on a distributed list building, where each vertex broadcasts basic information that only depends on its degree and that of its neighbours. A decentralised external process then unveils the community structure. The relevance of the proposed method is experimentally shown on both artificial and real data.Comment: 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Aug 2015, Paris, France. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Minin

Sparsity-Inducing Fuzzy Subspace Clustering

This paper considers a fuzzy subspace clustering problem and proposes to introduce an original sparsity-inducing regularization term. The minimization of this term, which involves a l$_{0}$ penalty, is considered from a geometric point of view and a novel proximal operator is derived. A subspace clustering algorithm, Prosecco, is proposed to optimize the cost function using both proximal and alternate gradient descent. Experiments comparing this algorithm to the state of the art in sparse fuzzy subspace clustering show the relevance of the proposed approach

Extraction de motifs graduels par corrélations d'ordres induits

National audienceGradual tendencies of the form the more X is A, the more Y is B linguistically express information about correlation between attributes and their covariations. In this paper, we present a comparative study of the various formalisations that have been proposed, studying their respective semantics and properties. We then propose an algorithm that combines the principles of existing approaches to efficiently extract frequent gradual itemsets, illustrating its use on a real data set.Les tendances graduelles de la forme plus X est A, plus Y est B expriment linguistiquement des informations sur les corrélations et co-variations des attributs. Dans cet article, nous présentons une étude comparative des formalisations qui ont été proposées, examinant leurs sémantiques et propriétés respectives. Nous proposons ensuite un algorithme qui combine les principes de plusieurs approches existantes pour extraire efficacement les motifs graduels fréquents et nous illustrons son utilisation sur une base de données réelle

Trust Dynamics: A Case-study on Railway Sensors

International audienceSensors constitute information providers which are subject to imperfections and assessing the quality of their outputs, in particular the trust that can be put in them, is a crucial task. Indeed, timely recognising a low-trust sensor output can greatly improve the decision making process at the fusion level, help solving safety issues and avoiding expensive operations such as either unnecessary or delayed maintenance. In this framework, this paper considers the question of trust dynamics, i.e. its temporal evolution with respect to the information flow. The goal is to increase the user understanding of the trust computation model, as well as to give hints about how to refine the model and set its parameters according to specific needs. Considering a trust computation model based on three dimensions, namely reliability, likelihood and credibility, the paper proposes a protocol for the evaluation of the scoring method, in the case when no ground truth is available, using realistic simulated data to analyse the trust evolution at the local level of a single sensor. After a visual and formal analysis, the scoring method is applied to real data at a global level to observe interactions and dependencies among multiple sensors

PANDA: Human-in-the-Loop Anomaly Detection and Explanation

International audienceThe paper addresses the tasks of anomaly detection and explanation simultaneously, in the human-in-the-loop paradigm integrating the end-user expertise: it first proposes to exploit two complementary data representations to identify anomalies, namely the description induced by the raw features and the description induced by a user-defined vocabulary. These representations respectively lead to identify so-called data-driven and knowledge-driven anomalies. The paper then proposes to confront these two sets of instances so as to improve the detection step and to dispose of tools towards anomaly explanations. It distinguishes and discusses three cases, underlining how the two description spaces can benefit from one another, in terms of accuracy and interpretability

Massive Data Exploration using Estimated Cardinalities

International audienceLinguistic summaries are used in this work to provide personalized exploration functionalities on massive relational data. To ensure a fluid exploration of the data, cardinalities of the data properties described in the summaries are estimated from statistics about the data distribution. The proposed workflow also involves a vocabulary inference mechanism from these statistics and a sampling-based approach to consolidate the estimated cardinalities. The paper shows that soft computing techniques are particularly relevant to build concrete and functional business intelligence solutions

PANDA : Personnaliser les ANomalies Détectées par Apprentissage

International audienceThe paper addresses the tasks of anomaly detection and explanation simultaneously, in the human-in-the-loop paradigm integrating the end-user expertise : it first proposes to exploit two complementary data representations to identify anomalies, namely the description induced by the raw features and the description induced by a userdefined vocabulary. These representations respectively lead to identify so-called data-driven and knowledge driven anomalies. The paper then proposes to confront these two sets of instances so as to improve the detection step and to dispose of tools towards anomaly explanations. It distinguishes and discusses three cases, underlining how the two description spaces can benefit from oneanother, in terms of accuracy and interpretability.La personnalisation par prise en compte de l'expertise de l'utilisateur des processus de détection d'anomalies est le sujet principal de cet article. Les anomalies sont identifiées dans deux espaces de représentation des données, l'espace initial composé des valeurs brutes et l'espace induit par un vocabulaire flou défini par l'utilisateur sur ces attributs initiaux. L'application de toute méthode de détection d'anomalies sur ces deux espaces conduit à différencier des anomalies issues des données brutes de celles issues du vocabulaire. Afin à la fois d'améliorer la détection des exceptions et de disposer d'outils pour les expliquer, ces deux ensembles d'anomalies sont confrontés. Trois situations émanant de cette comparaison sont étudiées afin de montrer comment l'utilisation combinée des deux espaces améliore l'efficacité de la procédure de détection d'anomalies ainsi que l'interprétabilité des résultats qu'elle génère