This paper offers a local distributed algorithm for expectation maximization in large peer-to-peer environments. The algorithm can be used for a variety of well-known data mining...
Data mining methods are successful in educational environments to discover new knowledge or learner skills or features. Unfortunately, they have not been used in depth with collabo...
Abstract In this paper we propose a novel parallel algorithm for frequent itemset mining. The algorithm is based on the filter-stream programming model, in which the frequent item...
Adriano Veloso, Wagner Meira Jr., Renato Ferreira,...
One way to exploit Thread Level Parallelism (TLP) is to use architectures that implement novel multithreaded execution models, like Scheduled DataFlow (SDF). This latter model pro...
Skyline queries help users make intelligent decisions over complex data, where different and often conflicting criteria are considered. Current skyline computation methods are rest...
Ping Wu, Caijie Zhang, Ying Feng, Ben Y. Zhao, Div...