Two-Phase Mining for Frequent Closed Episodes

Liao, Guoqiong; Yang, Xiaoting; Xie, Sihong; Yu, Philip S.; Wan, Changxuan

doi:10.1007/978-3-319-39937-9_5

Cited by 5 publications

(6 citation statements)

References 14 publications

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“…Many studies have been done on extracting concise representations of episodes to reduce the number of discovered episodes and have shown that those representations can provide better accuracy for various prediction tasks (Amiri et al, 2019;Ao et al, 2019;Li et al, 2018;Liao et al, 2016Liao et al, , 2018Ma et al, 2004;Méger & Rigotti, 2004;Tatti & Cule, 2011Zhou et al, 2010;Zhu et al, 2012Zhu et al, , 2011.…”

Section: Episodes With Concise Representationsmentioning

confidence: 99%

“…This property holds for frequency definitions that are anti-monotonic, and hence closed episodes are only used in this context. Some recent algorithms for mining frequent closed episodes include FCEMinner (Zhu et al, 2012), CloEpi (Zhou et al, 2010), MineEpisode (Tatti & Cule, 2012), and 2PEM (Liao et al, 2016).…”

Section: Closed Episodesmentioning

confidence: 99%

“…It is to be noted that many algorithms use modified definitions of the aforementioned definitions to mine episodes that are more appropriate for a specific domain or to process specific types of data. For instance, the NONEPI algorithm (Ouarem et al, 2021) relies on the concepts of suffix and prefix, which are special cases of the notion of sup‐episode, and the 2PEM algorithm (Liao et al, 2016) considers three types of sub‐episodes called forward‐extension, middle‐extension, and backward‐extension, in the context of closed episode mining, as it will be discussed later.…”

Section: Frequent Episode Miningmentioning

confidence: 99%

“…The most popular representations are maximal episodes (Ao et al, 2019), closed episodes (Amiri et al, 2019; Liao et al, 2016, 2018; Tatti & Cule, 2011, 2012; Zhou et al, 2010; Zhu et al, 2012), and generator episodes. Some of those representations, such as generator episodes, have been used to mine representative episode rules (Zhu et al, 2011).…”

Section: Extensions Of Traditional Episode Miningmentioning

confidence: 99%

“…Weighted episode mining can be viewed as a type of utility‐based episode mining framework, except that in the utility model, each event is associated with a weight. To the best of our knowledge, only Liao et al (2018) studied weighted episode mining. The problem definition is as follows: let there be a collection

D

of sequences, a user‐specified maximal window width

maxwin

minsup

and a parameter

minsup_wa

.…”

Section: Extensions Of Traditional Episode Miningmentioning

confidence: 99%

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A survey of episode mining

Ouarem,

Nouioua,

Fournier‐Viger

2023

WIREs Data Min & Knowl

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Episode mining is a research area in data mining, where the aim is to discover interesting episodes, that is, subsequences of events, in an event sequence. The most popular episode‐mining task is frequent episode mining (FEM), which consists of identifying episodes that appear frequently in an event sequence, but this task has also been extended in various ways. It was shown that episode mining can reveal insightful patterns for numerous applications such as web stream analysis, network fault management, and cybersecurity, and that episodes can be useful for prediction. Episode mining is an active research area, and there have been numerous advances in the field over the last 25 years. However, due to the rapid evolution of the pattern mining field, there is no prior study that summarizes and gives a detailed overview of this field. The contribution of this article is to fill this gap by presenting an up‐to‐date survey that provides an introduction to episode mining and an overview of recent developments and research opportunities. This advanced review first gives an introduction to the field of episode mining and the first algorithms. Then, the main concepts used in these algorithms are explained. After that, several recent studies are reviewed that have addressed some limitations of these algorithms and proposed novel solutions to overcome them. Finally, the paper lists some possible extensions of the existing frameworks to mine more meaningful patterns and presents some possible orientations for future work that may contribute to the evolution of the episode mining field.This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Algorithmic Development > Association Rules Technologies > Association Rules

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