We present algorithms for identifying frequently occurring items in a large distributed data set. Our algorithms use gossip as the underlying communication mechanism, and do not rely on any central control, nor on an underlying network structure, such as a spanning tree. Instead, nodes repeatedly select a random partner and exchange data with that partner. If this process continues for a (short...

We present FDCMSS, a new sketch based algorithm for mining frequent items in data streams. The algorithm cleverly combines key ideas borrowed from forward decay, the Count-Min and the Space Saving algorithms. It works in the time fading model, mining data streams according to the cash register model. We formally prove its correctness and show, through extensive experimental results, that our al...

We assume a dataset of transactions generated by a set of users over structured items where each item could be described through a set of features. In this paper, we are interested in identifying the frequent featuresets (set of features) by mining item transactions. For example, in a news website, items correspond to news articles, the features are the named-entities/topics in the articles and...

We study the problem of finding frequent items in a continuous stream of itemsets. A new frequency measure is introduced, based on a flexible window length. For a given item, its current frequency in the stream is defined as the maximal frequency over all windows from any point in the past until the current state. We study the properties of the new measure, and propose an incremental algorithm ...

a r t i c l e i n f o a b s t r a c t In this paper, we consider the problem of finding-approximate frequent items over a sliding window of size N. A recent work by Lee and Ting (2006) [7] solves the problem by giving an algorithm that supports O (1) query and update time, and uses O (1) space. Their query time and memory usage are essentially optimal, but the update time is not. We give a new ...

The time aspect is not currently taken into account for finding a region of interesting (ROI) or a hot region, so that due to the time to visit frequently a place cannot be determined, it is difficult to discover the visiting regularity for a moving object. To this end, the spatio-temporal item (STI) and frequent spatio-temporal item (FSTI) integrated spatial and temporal attributes are defined...