OBJECTIVE Automatic text summarization tools can help users in the biomedical domain to access information efficiently from a large volume of scientific literature and other sources of text documents. In this paper, we propose a summarization method that combines itemset mining and domain knowledge to construct a concept-based model and to extract the main subtopics from an input document. Our ...

Over the last decades, frequent itemset mining has become a major area of research, with applications including indexing and similarity search, as well as mining of data streams, web, and software bugs. Although several efficient techniques for generating frequent itemsets with a minimum support (frequency) have been proposed, the number of itemsets produced is in many cases too large for effec...

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 ...

The incremental mining of association rules has been shown to be more efficient than rerunning standard association rule algorithms such as Apriori. As each increment is processed, we see the emergence of some itemsets. An itemset that has emerged is one that was small and is large in the current increment. An emergent large itemset is a small itemset that has the potential to become large, and...

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 ...

We consider the problem of optimizing processing of batches of frequent itemset queries. The problem is a particular case of multiple-query optimization, where the goal is to minimize the total execution time of the set of queries. We propose an algorithm that is a combination of the Mine Merge method, previously proposed for processing of batches of frequent itemset queries, and the Partition ...

 Abstract—We design a new algorithm for identifying against-expectation patterns. An against-expectation pattern is either an itemset whose support is out of a range of the expected support value, referred to as an against-expectation itemset, or it is an association rule generated by an against-expectation itemset, referred to as an against-expectation rule. Therefore, against-expectation pat...