A framework for mining interesting high utility patterns with a strong frequency affinity

PrePost+: An efficient N-lists-based algorithm for mining frequent itemsets via Children–Parent Equivalence pruning

“…In recent years, probabilistic frequent itemsets (Aggarwal, Li, & Wang, 2009;Chui, Kao, & Hung, 2007;Tong, Chen, Cheng, & Yu, 2012) and high utility itemsets (Ahmed, Tanbeer, Jeong, & Choi, 2011;Lin, Tu, & Hsueh, 2012;Tseng, Wu, Shie, & Yu, 2010;Yin, Zheng, & Cao, 2012) have received a great deal of attention due to their wide applications. Mining probabilistic frequent itemsets and high utility itemsets has emerged as hot topics in the field of pattern mining.…”

Correlated utility-based pattern mining

“…In the area of utility-oriented pattern mining, only few studies concern the utility and correlation together to derive the desired patterns. For instance, the HUIPM [23] and FDHUP [24] algorithms consider both frequency-affinity and utility as the two key measures to derive the desired patterns. However, the co-occurrence frequency of transactions is regarded as the correlation factor.…”

“…Hence, it is a critical issue to address the effectiveness problem for discovering positively correlated and high-utility patterns based on the utility and correlation measures. In the past few decades, some well-known correlation measures, e.g., the support [20,21], confidence [21], all-confidence [22], frequency affinity [23], and coherence [22], have been studied in data mining. In the field of utility mining, the HUIPM [23] and FDHUP [24] algorithms were proposed to discover high-utility interesting patterns (HUIPs) with strong frequency affinity.…”

“…In the field of utility mining, the HUIPM [23] and FDHUP [24] algorithms were proposed to discover high-utility interesting patterns (HUIPs) with strong frequency affinity. The concept of affinity utility is introduced in HUIPM [23]. However, the tree-based HUIPM algorithm is time-consuming and may lead to the problem of combinatorial explosion.…”

An efficient approach to mine flexible periodic patterns in time series databases

“…Some of the pattern mining techniques include frequent pattern mining (Ahmed et al, 2012(Ahmed et al, , 2011Han et al, 2000;Duong et al, 2014), sequential pattern mining (Srikant and Agrawal, 1996;Agrawal and Srikant, 1995), closed-frequent pattern mining (Kim et al, 2004;Pei et al, 2000;Zaki and Hsiao, 2005), episode mining (Mannila et al, 1997), event labeling (Fanaee-T and Gama, 2014) and inter-transaction pattern mining (Tung et al, 1999). Similarly, in time series databases, periodic pattern mining is one of the most important research problems.…”