a r t i c l e i n f o a b s t r a c t We study the classic frequent items problem in data streams, but from a competitive analysis point of view. We consider the standard worst-case input model, as well as a weaker distributional adversarial setting. We are primarily interested in the single-slot memory case and for both models we give (asymptotically) tight bounds of Θ(√ N) and Θ(3 √ N) respec...

We address the problem of discovering frequent items in unstructured P2P networks which is relevant for several distributed services such as cache management, data replication, query refinement, topology optimization and security. This study makes the following contributions to the current state of the art. First, we propose and develop a fully distributed Protocol for Frequent ItemsDiscovery (...

The problem of frequent item discovery in streaming data has attracted a lot of attention lately. While the above problem has been studied extensively, and several techniques have been proposed for its solution, these approaches treat all the values of the data stream equally. Nevertheless, not all values are of equal importance. In several situations, we are interested more in the new values t...

In the area of data mining, the process of frequent pattern extraction finds interesting information about the association among the items in a transactional database. The notion of support is employed to extract the frequent patterns. Normally, a frequent pattern may contain items which belong to different categories of a particular domain. The existing approaches do not consider the notion of...

The problem of finding association rules from a dataset is to find all possible associations that hold among the items, given a minimum support value and a minimum confidence. This involves finding frequent sets first and then the association rules that hold within the items in the frequent sets. The problem of mining temporal association rules from temporal dataset is to find association rules...

Mining association rules in large database is one of most popular data mining techniques for business decision makers. Discovering frequent item set is the core process in association rule mining. Numerous algorithms are available in the literature to find frequent patterns. Apriori and FP-tree are the most common methods for finding frequent items. Apriori finds significant frequent items usin...

Mining frequent itemsets from transactional data streams is challenging due to the nature of the exponential explosion of itemsets and the limit memory space required for mining frequent itemsets. Given a domain of I unique items, the possible number of itemsets can be up to 2 1. When the length of data streams approaches to a very large number N, the possibility of an itemset to be frequent be...

Data mining is the collection of techniques for the resourceful, automatic discovery of previously unknown, suitable, novel, helpful and understandable patterns in large databases. Frequent pattern mining has emerged as a vital task in data mining. Frequent patterns are those that occur frequently in a data set. In traditional frequent pattern mining, patterns and items within the patterns are ...