We prove, under mild conditions, that the stochastic gradient Langevin dynamics converges to a limiting law as time tends infinity, even in case where driving data sequence is dependent.

Mining data streams is an emerging area of research given the potentially large number of business and scientific applications. A significant challenge in analyzing/mining data streams is the high data rate of the stream. In this paper, we propose a novel approach to cope with the high data rate of incoming data streams. We termed our approach “algorithm output granularity”. It is a resource-aw...

The frequent items problem is to process a stream of items and find all items occurring more than a given fraction of the time. It is one of the most heavily studied problems in data stream mining, dating back to the 1980s. Many applications rely directly or indirectly on finding the frequent items, and implementations are in use in large scale industrial systems. However, there has not been mu...