Business intelligence is information about a company's past performance that is used to help predict the company's future performance. It can reveal emerging trends from which the company might profit [31]. Data mining allows users to sift through the enormous amount of information available in data warehouses; it is from this sifting process that business intelligence gems may be found [31]. W...

The problem of selecting a small, yet high quality subset of patterns from a larger collection of itemsets has recently attracted a lot of research. Here we discuss an approach to this problem using the notion of decomposable families of itemsets. Such itemset families define a probabilistic model for the data from which the original collection of itemsets was derived. Furthermore, they induce ...

Frequent itemset mining over dynamic data is an important problem in the context of data mining. The two main factors of data stream mining algorithm are memory usage and runtime, since they are limited resources. Mining frequent pattern in data streams, like traditional database and many other types of databases, has been studied popularly in data mining research. Many applications like stock ...

Résumé. Nous présentons dans cet article un nouvel automate : le FIA (Frequent Itemset Automaton) pour traiter de façon efficace la problématique de l’extraction des itemsets fréquents dans les flots de données. Le FIA est une structure de données très compacte et informative qui présente également des propriétés incrémentales intéressantes pour les mises à jour avec une granularité très fine. ...

Previous work on frequent itemset mining has focused on finding all itemsets that are frequent in a specified part of a database. In this paper, we motivate the dual question of finding under what circumstances a given itemset satisfies a pattern of interest (e.g., frequency) in a database. Circumstances form a lattice that generalizes the instance lattice associated with datacube. Exploiting t...

The Apriori algorithm’s frequent itemset approach has become the standard approach to discovering association rules. However, the computation requirements of the frequent itemset approach are infeasible for dense data and the approach is unable to discover infrequent associations. OPUS AR is an efficient algorithm for association rule discovery that does not utilize frequent itemsets and hence ...

Frequent itemset mining is a classic problem in data mining. It is a non-supervised process which concerns in finding frequent patterns (or itemsets) hidden in large volumes of data in order to produce compact summaries or models of the database. These models are typically used to generate association rules, but recently they have also been used in far reaching domains like e-commerce and bio-i...

Frequent itemset mining is one of fundamental data mining problems that shares many similarities with traditional database querying. Hence, several query optimization techniques known from database systems have been successfully applied to frequent itemset queries, including reusing results of previous queries and multi-query optimization. In this paper, we consider a new problem of processing ...