We present a 1-pass algorithm for estimating the most frequent items in a data stream using very limited storage space. Our method relies on a novel data structure called a count sketch, which allows us to estimate the frequencies of all the items in the stream. Our algorithm achieves better space bounds than the previous best known algorithms for this problem for many natural distributions on ...

Frequent Item-set Mining (FIM), sometimes called Market Basket Analysis (MBA) or Association Rule Learning (ARL), are Machine Learning (ML) methods for creating rules from datasets of transactions of items. Most methods identify items likely to appear together in a transaction based on the support (i.e. a minimum number of relative co-occurrence of the items) for that hypothesis. Although this ...

We present novel space and time-efficient algorithms for finding frequent items over general update streams. Our algorithms are based on a novel adaptation of the popular dyadic intervals method for finding frequent items. The algorithms improve upon existing algorithms in both theory and practice.

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...

Documents exist in different formats. When we have document images, in order to access some part, preferably all, of the information contained in that images, we have to deploy a document image analysis application. Document images can be mostly textual or mostly graphical. If, for a user, a task is to retrieve document images, relevant to a query from a set, we must use indexing techniques. Th...

MONITORING FREQUENT ITEMS OVER DISTRIBUTED DATA STREAMS Robert H. Fuller April 3, 2007 Many important applications require the discovery of items which have occurred frequently. Knowledge of these items is commonly used in anomaly detection and network monitoring tasks. Effective solutions for this problem focus mainly on reducing memory requirements in a centralized environment. These solution...