Relational data, where objects are defined by similarities or dissimilarities, is omnipresent and essential in real clustering applications. In this context, relational clustering, which aims to identify groups of similar objects based on their mutual relationships, has become a necessity. However, most existing relational clustering methods cannot effectively handle multi-view data sets while representing uncertainty and imprecision when faced with objects in overlapping clusters. To address this gap, we introduce a new relational clustering method, called Multi-View Evidential C-Medoid clustering with adaptive weightings (MECMdd). Our approach is based on the theory of belief functions to characterize the partial knowledge in cluster assignment. It integrates view weight assignments, estimated locally for each cluster and globally in a collaborative learning framework. We have evaluated our proposition via several experiments using different real-world datasets, compared to other related and state-of-the-art methods, in terms of their advantages and overall effectiveness.

Deep neural networks are important for a wide range of scientific and industrial processes. However, a classical discriminative model always makes a classification with respect to the probabilities allocated to the training labels, even when the sample is out of the domain. Thus, it is of interest to assign uncertainty to a model prediction to avoid such a situation. Fortunately, there are many existing methods for dealing with this kind of problem, one branch of which involves combining neural networks with subjective logic (SL). Based on previous works, we propose a new method called subjective-logic-based uncertainty estimation (SLUE) that can take the base rate distribution explicitly into account to refine the Dirichlet distribution parameters and guide the model training. Experiments were performed on several public datasets and additional adversarial datasets. Compared with existed methods, SLUE reached better uncertainty assessment performance (15% improvement in terms of % max entropy) as well as comparable prediction accuracy performance.

Hard and fuzzy clustering algorithms are part of the partition-based clustering family. They are widely used in real-world applications to cluster numerical and categorical data. While in hard clustering an object is assigned to a cluster with certainty, in fuzzy clustering an object can be assigned to different clusters given a membership degree. For both types of method an entropy can be incorporated into the objective function, mostly to avoid solutions raising too much uncertainties. In this paper, we present an extension of a fuzzy clustering method for categorical data using fuzzy centroids. The new algorithm, referred to as Categorical Fuzzy Entropy (CFE), integrates an entropy term in the objective function. This allows a better fuzzification of the cluster prototypes. Experiments on ten real-world data sets and statistical comparisons show that the new method can efficiently handle categorical data.

We present a temporal Bayesian filter for semantic segmentation of a video sequence. Each pixel is a random variable following a discrete probabilistic distribution function representing possible semantic classes. Bayesian filtering consists in two main steps: 1) a prediction model and 2) an observation model (likelihood). We propose to use a datadriven prediction function derived from a dense optical flow between images t and t + 1 achieved by a deep neural network [1]. Moreover, the observation function uses a semantic segmentation network. The resulting approach is evaluated on the public dataset Cityscapes. We show that using the temporal filtering increases the accuracy of the semantic segmentation.

Evidential clustering methods assign objects to clusters with a degree of belief, allowing for better representation of cluster overlap and outliers. Based on the theoretical framework of belief functions, they generate credal partitions which extend crisp, fuzzy and possibilistic partitions. Despite their ability to provide rich information about the partition, no evidential clustering algorithm for categorical data has yet been proposed. This paper presents a categorical version of ECM, an evidential variant of k-means. The proposed algorithm, referred to as catECM, considers a new dissimilarity measure and introduces an alternating minimization scheme in order to obtain a credal partition. Experimental results with real and synthetic data sets show the potential and the efficiency of cat-ECM for clustering categorical data.

Clustering methods assign objects to clusters using only as prior information the characteristics of the objects. However, clustering algorithms performance can be improved when background knowledge is available. Such background knowledge can be incorporated in a clustering method as label constraints which results in a semi-supervised clustering algorithm. We propose to extend two possibilistic clustering algorithms to make use of available a priori information. The goal is twofold: to improve the accuracy of the clustering result by leading the method towards a desired solution and to detect outliers by taking advantage of the generated possibilistic partition. The proposed methods are called semi-supervised repulsive possibilistic c-means (SRPCM) and semi-supervised possibilistic fuzzy c-means (SPFCM). They correspond to possibilistic clustering algorithms that introduce label constraints. Experimental results show that the proposed algorithms using label constraints improve (1) the clustering result and (2) the outliers detection.

Fuzzy or hard partitioning methods aim at grouping objects according to their similarity. Recently, a new concept of partition based on belief function theory, called credal partition, has been proposed and has been shown to generate meaningful description of the data. Hard, fuzzy or credal partitions are generally obtained using unsupervised learning methods, using only the numeric description between two objects to compute their similarity. However, in some applications, some kind of background knowledge about the objects or about the clusters is available. To integrate this auxiliary information, constraint-based (or semi-supervised) methods have been proposed. A popular type of constraints specifies whether two objects are in the same cluster (must-link) or in different clusters (cannot-link). We propose here a new algorithm, called CECM, which computes a credal partition using a constrained clustering method. We show how to translate the available information into constraints, and how to integrate them in the search of the credal partition. The paper ends with some experimental results. Results of CECM are compared to other constrained clustering algorithms. Then an application in image segmentation is described.