Frequent pattern mining (FPM) has become one of the most popular data mining approaches for the analysis of purchasing patterns. Methods such as Apriori and FP-growth have been shown to work efficiently in this setting. However, these techniques are typically restricted to a single concept level. Since typical business databases support hierarchies that represent the relationships amongst many ...

A major challenge in frequent-pattern mining is the sheer size of its mining results. In many cases, a high min sup threshold may discover only commonsense patterns but a low one may generate an explosive number of output patterns, which severely restricts its usage. In this paper, we study the problem of compressing frequent-pattern sets. Typically, frequent patterns can be clustered with a ti...

A significant data mining issue is the effective discovery of association rules. The extraction of association rules faces the problem of the combinatorial explosion of the search space, and the loss of information by the discretization of values. The first problem is confronted effectively by the Frequent Pattern Tree approach of [10]. This approach avoids the candidate generation phase of Apr...

Association rule learning is a popular and well researched technique for discovering interesting relations between variables in large databases in the area of data mining. The association rules are a part of intelligent systems. Association rules are usually required to satisfy a user-specified minimum support and a user-specified minimum confidence at the same time. Apriori and FPGrowth algori...

Frequent itemsets play an essential role in many data mining tasks that try to find interesting patterns from databases, such as association rules, correlations, sequences, episodes, classifiers, clusters and many more of which the mining of association rules is one of the most popular problems. The original motivation for searching association rules came from the need to analyze so called supe...

Frequent pattern mining is an essential data mining task, with a goal of discovering knowledge in the form of repeated patterns. Many efficient pattern mining algorithms have been discovered in the last two decades, yet most do not scale to the type of data we are presented with today, the so-called “Big Data”. Scalable parallel algorithms hold the key to solving the problem in this context. In...