Efficient representative pattern mining based on weight and maximality conditions

Abstract

As a core area in data mining, frequent pattern (or itemset) mining has been studied for a long time. Weighted frequent pattern mining prunes unimportant patterns and maximal frequent pattern mining discovers compact frequent patterns. These approaches contribute to improving mining performance by reducing the search space. However, we need to consider both the downward closure property and patterns' subset checking process when integrating these different methods in order to prevent unintended pattern losses. Moreover, it is also essential to extract valid patterns with faster runtime and less memory consumption. For this reason, in this paper, we propose more efficient maximal weighted frequent pattern (MWFP) mining approaches based on tree and array structures. We describe how to handle these problems more efficiently, maintaining the correctness of our method. We develop two types of maximal weighted frequent mining algorithms based on weight ascending order and support descending order and compare these two algorithms to conclude which is more suitable for MWFP mining. In addition, comprehensive tests in this paper show that our algorithms are more efficient and scalable than state‐of‐the‐art algorithms, and they also have the correctness of the MWFP mining in terms of their pattern generation results.

An efficient method for mining sequential patterns with indices

“…Those types of patterns allow us to generate all the frequent patterns in the databases. Researchers also proposed more ways to limit the patterns found, such as inter-constraint [6], maximal constraint [40], or gap constraint approaches [41]. Another issue is high utility SPM or weighted SPM.…”

Improving a state‐of‐the‐art heuristic for the minimum latency problem with data mining

“…Although this implementation was presented in 2003, according to Yun et al. (2016), FPMax* is still considered as a state‐of‐the‐art algorithm along with MAFIA and LCM algorithms. In the context of our work, each solution (or permutation) of the elite set is transformed into a set of (arc) identifiers.…”

DPARM: Differentially Private Association Rules Mining

“…Several wellknown algorithms for association rule mining have been developed, such as Apriori [2], Eclat [39], FP-Growth [10], and NR-HARs [22] algorithms. Some well-konwn studies [38] [36] have attempted to elaborate efficient frequent itemset/pattern mining algorithms for transactional datasets or the data from the Internet of Things. These works focus on designing efficient algorithms for mining frequent itemsets/patterns based on novel data structures and mining techniques while maintaining high utility.…”