. Mining frequent log items is an active area in data mining that aims at searching interesting relationships between items in databases. It can be used to address a wide variety of problems such as discovering association rules, sequential patterns, correlations and much more. Weblog that analyzes a Web site's access log and reports the number of visitors, views, hits, most frequently visited ...

In data mining research, generating frequent items from large databases is one of the important issues and the key factor for implementing association rule mining tasks. Mining infrequent items such as relationships among rare but expensive products is another demanding issue which have been shown in some recent studies. Therefore this study considers user assigned threshold values as a constra...

In this paper, we strengthen the competitive analysis results obtained for a fundamental online streaming problem, the Frequent Items Problem. Additionally, we contribute with a more detailed analysis of this problem, using alternative performance measures, supplementing the insight gained from competitive analysis. The results also contribute to the general study of performance measures for on...

The primary task of association rule mining is to detect frequently co-occurring groups of items in transactional databases. The intention is to use this knowledge for prediction purposes. So many researches has focused mainly on how to expedite the search for frequently co-occurring groups of items in "shopping cart" and less attention has been paid to the methods that exploit these ...

Association rules describe relationship between items in a transactional database. Intuitively, a rule X → Y implies that when X occurs in a transaction, Y is likely to occur as well. Apriori is the most wellknown method to extract association rules from a data set (Agrawal et al., 1993; Agrawal & Srikant, 1994). It works in two major steps. First, frequent itemsets, or sets of items whose supp...

The success of knowledge discovery in real-world domains often depends on our ability to handle data imperfections. Here we study this problem in the framework of association mining, seeking to identify frequent itemsets in transactional databases where the presence of some items in a given transaction is unknown. We want to use the frequent itemsets to predict “missing items”: based on the par...

Real life transaction data often miss some occurrences of items that are actually present. As a consequence some potentially interesting frequent item sets cannot be discovered, since with exact matching the number of supporting transactions may be smaller than the user-specified minimum. In order to allow approximate matching during the mining process, we propose an approach based on transacti...

In this paper, we propose an algorithm called PITP-Miner that utilizes a projection based approach to mine frequent inter-transaction patterns efficiently. The algorithm only searches for local frequent items in a projected database that stores potential local inter-transaction items and partitions the database into a set of smaller databases recursively. In addition, two pruning strategies are...

In this paper, we strengthen the competitive analysis results obtained for a fundamental online streaming problem, the Frequent Items Problem. Additionally, we contribute with a more detailed analysis of this problem, using alternative performance measures, supplementing the insight gained from competitive analysis. The results also contribute to the general study of performance measures for on...

MOTIVATION In genome-wide association studies (GWAS), up to millions of single nucleotide polymorphisms (SNPs) are genotyped for thousands of individuals. However, conventional single locus-based approaches are usually unable to detect gene-gene interactions underlying complex diseases. Due to the huge search space for complicated high order interactions, many existing multi-locus approaches ar...