A clique clustering of a graph is a partitioning of its vertices into disjoint cliques. The quality of a clique clustering is measured by the total number of edges in its cliques. We consider the online variant of the clique clustering problem, where the vertices of the input graph arrive one at a time. At each step, the newly arrived vertex forms a singleton clique, and the algorithm can merge...

We introduce a new online skill discovery method for reinforcement learning in discrete domains. The method is based on the bottleneck principle and identifies skills using a bottom-up hierarchical clustering of the estimated transition graph. In contrast to prior clustering approaches, it can be used incrementally and thus several times during the learning process. Our empirical evaluation sho...

We present a new fast online clustering algorithm that reliably recovers arbitrary-shaped data clusters in high throughout data streams. Unlike the existing state-of-the-art online clustering methods based on k-means or k-medoid, it does not make any restrictive generative assumptions. In addition, in contrast to existing nonparametric clustering techniques such as DBScan or DenStream, it gives...

In this paper an approach that is using evolving, incremental (on-line) clustering to automatically group relevant Web-based documents is proposed. It is centred on a recently introduced evolving fuzzy rule-based clustering approach and borrows heavily from the Nature in the sense that it is evolution-inspired. That is, the structure of the clusters and their number is not predefined, but it se...

This paper shows that one can be competitive with the kmeans objective while operating online. In this model, the algorithm receives vectors v1, . . . , vn one by one in an arbitrary order. For each vector vt the algorithm outputs a cluster identifier before receiving vt+1. Our online algorithm generates Õ(k) clusters whose k-means cost is Õ(W ∗) where W ∗ is the optimal k-means cost using k cl...

This paper aims to address the problem of profiling human activities captured in surveillance videos for the applications of online normal human activity recognition and anomaly detection. A novel framework is developed for automatic human activity modeling and online anomaly detection without any manual labeling of the training dataset. The framework consists of the following key components: 1...

BACKGROUND We hypothesize that epidemics around their onset tend to affect primarily a well-defined subgroup of the overall population that is for some reason particularly susceptible. While the vulnerable cohort is often well described for many human diseases, this is not the case for instance when we wish to detect a novel computer virus. Clustering may be used to define the subgroups that wi...

This supplementary material contains all proofs and technical details omitted from the main text, along with ancillary comments, discussion about related work, and extra experimental results. 1. Proof of Theorem 1 The following sequence of lemmas are of preliminary importance. The first one needs extra variance conditions on the process X generating the context vectors. We find it convenient to...

Online document clustering takes as its input a list of document vectors, ordered by time. A document vector consists of a list of K terms and their associated weights. The generation of terms and their weights from the document text may vary, but the TF-IDF (term frequency-inverse document frequency) method is popular for clustering applications [1]. The assumption is that the resulting docume...

All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes ...