Abstract. 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

Abstract. Monitoring frequently occuring items is a recurring task in a variety of applications. Although a number of solutions have been proposed there has been few to address the problem in a distributed networked environment. Most past solutions relied upon approximating results to lower

. In Transactional Database, each transaction consists of items purchased by the customer. One of the basic market basket analysis algorithms is an Apriori, which generate all possible frequent patterns. In this research paper we describe the improved algorithm of dynamic Programming approach. This algorithm

Abstract. In today's Internet, there are large amounts of data spreading over many physical distributed nodes which makes it impractical to send all the data to one central node for query processing. Finding distributed frequent items is important for a number of applications such as DDo

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

Computing statistical information on probabilistic data has attracted a lot of attention recently, as the data generated from a wide range of data sources are inherently fuzzy or uncertain. In this paper, we study an important statistical query on probabilistic data: finding the frequent items. One

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doi:10.1145/1562764.1562789 The frequent items problem is to process a stream of items and find all those which occur more than a given fraction of the time. It is one of the most heavily studied problems in mining data streams, dating back to the 1980s. Many other applications rely directly

Abstract: Due to various reasons transaction data often lack information about some items. This leads to the problem that some potentially interesting frequent item sets cannot be discovered, since by exact matching the number of supporting transactions may be smaller than the user

Frequent item counting is one of the most important operations in time series data mining algorithms, and the space saving algorithm is a widely used approach to solving this problem. With the rapid rising of data input speeds, the most challenging problem in frequent item counting is to meet