Résumé. Le clustering semi-supervisé combine l’apprentissage supervisé and non-supervisé pour produire meilleurs clusterings. Dans la phase initiale supervisée de l’algorithme, un échantillon d’apprentissage est produit par selection aléatoire. On suppose que les exemples de l’échantillon d’apprentissage sont étiquetés par un attribut de classe. Puis, un algorithme incrémentiel développé pour l...

An effective way to improve the performance of deep neural networks in most computer vision tasks is quantity labeled data and quality labels. However, analysis processing medical images, high-quality annotation depends on experience professional knowledge experts, which makes it very difficult obtain a large number annotations. Therefore, we propose new semi-supervised framework for image clas...

Multi-view clustering has attracted much attention thanks to the capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most them generally overlook significance weakly-supervised and fail preserve feature properties multiple views, thus resulting unsatisfactory performance. To address these issues, this paper, we propose a novel...

Network attacks of the distributed denial service (DDoS) form are used to disrupt server replies and services. It is popular because it easy set up challenging detect. We can identify DDoS on network traffic in a variety ways. However, most effective methods for detecting identifying attack machine learning approaches. This considered be among dangerous internet threats. In order supervised alg...

Semi-supervised (or partial) fuzzy clustering plays an important and unique role in discovering hidden structure in data realized in presence of a certain quite limited fraction of labeled patterns. The objective of this study is to investigate and quantify the effect of various distance functions (distances) on the performance of the clustering mechanisms. The underlying goal of endowing the c...

For the problem of change detection it is difficult to have sufficient amount of ground truth information that is needed in supervised learning. On the contrary it is easy to identify a few labeled patterns by the experts. In this situation to avoid wastage of available information semi-supervision is suggestible to enhance the performance of unsupervised ones. Here we present the fuzzy cluster...

Keywords: Semi-supervised learning technique Probe vehicle trajectory Mixed corridor Urban expressway a b s t r a c t This paper proposes three enhanced semi-supervised clustering algorithms, namely the Constrained-K-Means (CKM), the Seeded-K-Means (SKM), and the Semi-Supervised Fuzzy c-Means (SFCM), to identify probe vehicle trajectories in the mixed traffic corridor. The proposed algorithms a...

Traditional clustering algorithms usually rely on a pre-defined similarity measure between unlabelled data to attempt to identify natural classes of items. When compared to what a human expert would provide on the same data, the results obtained may be disappointing if the similarity measure employed by the system is too different from the one a human would use. To obtain clusters fitting user ...