This chapter will provide a detailed survey of frequent pattern mining algorithms. A wide
variety of algorithms will be covered starting from Apriori. Many algorithms such as Eclat …

Greedy randomized adaptive search procedures, or GRASP, were introduced by T. Feo and
M. Resende in 1989 as a probabilistic heuristic for solving hard set covering problems. Soon …

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 …

This paper presents a new scalable algorithm for discovering closed frequent itemsets, a
lossless and condensed representation of all the frequent itemsets that can be mined from a …

This paper considers discovering frequent itemsets in transactional databases and
addresses the time complexity problem by using high performance computing (HPC). Three …

Association rule mining is a popular data mining task, which has important in many domains.
Because the task of association rule mining is very time consuming, evolutionary and swarm …

We explore in this paper a practicably interesting mining task to retrieve top-k (closed)
itemsets in the presence of the memory constraint. Specifically, as opposed to most previous …

Cycles are fundamental to human health and behavior. Examples include mood cycles,
circadian rhythms, and the menstrual cycle. However, modeling cycles in time series data is …

Frequent sets lie at the basis of many Data Mining algorithms. As a result, hundreds of
algorithms have been proposed in order to solve the frequent set mining problem. In this …

In this work, we propose a hybridization of GRASP metaheuristic that incorporates a data
mining process. We believe that patterns obtained from a set of sub-optimal solutions, by …