Clustering of high dimensional data streams is an important problem in many application domains, a prominent example being network monitoring. Several approaches have been lately ...
Irene Ntoutsi, Arthur Zimek, Themis Palpanas, Peer...
The k-NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to con...
Jing Wang, Jingdong Wang, Gang Zeng, Zhuowen Tu, R...
— Differential privacy has recently emerged as the de facto standard for private data release. This makes it possible to provide strong theoretical guarantees on the privacy and ...
Graham Cormode, Cecilia M. Procopiuc, Divesh Sriva...
In this paper we propose a robust classification rule for skewed unimodal distributions. For low dimensional data, the classifier is based on minimizing the adjusted outlyingness t...
Abstract. Data declustering speeds up large data set retrieval by partitioning the data across multiple disks or sites and performing retrievals in parallel. Performance is determi...
Hak-Cheol Kim, Mario A. Lopez, Scott T. Leutenegge...