This paper comprehensively surveys the development of trajectory clustering. Considering the critical role of trajectory data mining in modern intelligent systems for surveillance security, abnormal behavior detection, crowd behavior analysis, and traffic control, trajectory clustering has attracted growing attention. Existing trajectory clustering methods can be grouped into three categories: ...

— Clustering ensemble is one of the most recent advances in unsupervised learning. It aims to combine the clustering results obtained using different algorithms or from different runs of the same clustering algorithm for the same data set, this is accomplished using on a consensus function, the efficiency and accuracy of this method has been proven in many works in literature. In the first part...

This paper focuses on semi-supervised clustering, where the goal is to cluster a set of data-points given a set of similar/dissimilar examples. These examples provide instance-level equivalence/in-equivalence constraints (e.g., similar pairs belong to the same cluster while dissimilar pairs belong to different clusters), but in order to aid final clustering we must propagate them to feature-spa...

Nowadays, data are generated massively and rapidly from scientific fields such as bioinformatics, neuroscience and astronomy to business and engineering fields. Cluster analysis, as one of the major data analysis tools, is therefore more significant than ever. Here, we propose an effective Semi-supervised Divisive Clustering algorithm (SDC). Data points are first organized by a minimal spanning...

Incorporating background knowledge into unsupervised clustering algorithms has been the subject of extensive research in recent years. Nevertheless, existing algorithms implicitly assume that the background information, typically specified in the form of labeled examples or pairwise constraints, has the same feature space as the unlabeled data to be clustered. In this paper, we are concerned wi...

Semi-supervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of must-link and cannotlink constraints between pairs of examples. This paper presents a pairwise constrained clustering framework and a new method for actively selecting informative pairwise constraints to get improved clustering performance. The clust...

Semi-supervised clustering is an very important topic in machine learning and computer vision. The key challenge of this problem is how to learn a metric, such that the instances sharing the same label are more likely close to each other on the embedded space. However, little attention has been paid to learn better representations when the data lie on non-linear manifold. Fortunately, deep lear...

Inductive learning approaches traditionally categorized as supervised, which use labeled data sets, and unsupervised, which use unlabeled data sets in learning tasks. The great volume of available data and the cost involved in manual labeling has motivated the investigation of different solutions for machine learning tasks related to unlabeled data. The approach proposed here fits into this con...