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...

In this paper we show the potential of contextual itemset mining in the context of Linked Open Data. Contextual itemset mining extracts frequent associations among items considering background information. In the case of Linked Open Data, the background information is represented by an Ontology defined over the data. Each resulting itemset is specific to a particular context and contexts can be...

ABSTRACT For a transaction database, a frequent itemset is an itemset included in at least a specified number of transactions. To find all the frequent itemsets, the heaviest task is the computation of frequency of each candidate itemset. In the previous studies, there are roughly three data structures and algorithms for the computation: bitmap, prefix tree, and array lists. Each of these has i...

This paper provides a survey of the itemset method for association rule generation. The paper discusses past research on the topic and also studies the relevance and importance of the itemset method in generating association rules. We discuss a number of variations of the association rule problem which have been proposed in the literature and their practical applications. Some inherent weakness...

In this paper, we examine the issue of mining association rules among items in a large database of sales transactions. The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items which appear in a suucient number of transactions. The problem of discovering large itemsets can be solved by constructing a candidate set of i...

Mining frequently appearing patterns in a database is a basic problem in informatics, especially in data mining. Particularly, when the input database is a collection of subsets of an itemset, the problem is called the frequent itemset mining problem, and has been extensively studied. In the real-world use, one of difficulties of frequent itemset mining is that real-world data is often incorrec...

Frequent itemset counting is the first step for most association rule algorithms and some classification algorithms. It is the process of counting the number of occurrences of a set of items that happen across many transactions. The goal is to find those items which occur together most often. Expressing this functionality in RDBMS engines is difficult for two reasons. First, it leads to extreme...