Mining Frequent Partite Episodes with Partwise Constraints

Katoh, Takashi; Tago, S.; Asai, Tetsuya; Morikawa, Hisashi; Shigezumi, Junichi; Inakoshi, Hiroya

doi:10.1007/978-3-319-08407-7_8

Cited by 3 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Window-based frequency Yes WINEPI (Mannila et al, 1997) EpiBF (Casas-Garriga, 2003) WinMiner (Méger & Rigotti, 2004) Head frequency No EMMA (Huang & Chang, 2008) Total frequency Yes FEM-DFS (Zhu et al, 2018) Noninterleaved frequency No NOE-WinMiner (Wu et al, 2012) Occurrence-based Minimal occurrence-based No MINEPI (Mannila et al, 1997) MEELO (Ao et al, 2018) PartiteCD (Katoh et al, 2013) Nonoverlapped occurrence-based Yes NONEPI (Ouarem et al, 2021) POERM (Fournier-Viger et al, 2021) Distinct occurrence-based Yes ONCE+ (Li et al, 2018)…”

Section: Window-basedmentioning

confidence: 99%

A survey of episode mining

Ouarem,

Nouioua,

Fournier‐Viger

2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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

show abstract

Section: Window-basedmentioning

confidence: 99%

A survey of episode mining

Ouarem,

Nouioua,

Fournier‐Viger

2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…In general, the existing frequent episode mining algorithms can be classified into Apriori-based breadth-first algorithms [2-4, 11-13] and projection-based depth-first algorithms [5,7,10]. [2] presented two kinds of Apriori-based frequent episode mining methods, WINEPI and MINEPI.…”

Section: Related Workmentioning

confidence: 99%

“…In terms of episode mining in complex sequences, [13] proposed two frequent episodes mining algorithms, MINEPI+ and EMMA. [5] presented an algorithm that can find all frequent partite episodes satisfying a partwise constraint in an input sequence. [7] used the event tree structure to represent the sets of event types with paths and nodes, and to mine the frequent episodes by a recursive procedure.…”

Section: Related Workmentioning

confidence: 99%

“…Over the years, many episode mining methods, including frequent episode mining [2][3][4][5][6][7][8][9][10][11][12][13], closed episode mining [14][15][16], episode rule mining [17,18] and utility episode mining [19], have been proposed to discover temporal correlations successfully in many application domains such as alarm sequence analysis in telecommunication networks [2], user-behaviour prediction for web applications [20], stock trend analysis based on financial events [7], etc.…”

Section: Introductionmentioning

confidence: 99%

“…• As sequential pattern discovery, the existing episode mining algorithms can be roughly classified into A priori-based breadth-first algorithms [2-4, 11-13] and projection-based depth-first algorithms [5,7,10,14]. Since these two kinds of algorithms are level-wise pattern growth methods, i.e.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mining Weighted Frequent Closed Episodes over Multiple Sequences

et al. 2018

View full text Add to dashboard Cite

-Frequent episode discovery is introduced to mine useful and interesting temporal patterns from sequential data. The existing episode mining methods mainly focused on mining from a single long sequence consisting of events with time constraints. However, there can be multiple sequences of different importance as the persons or entities associated with each sequence can be of different importance. Aiming to mine episodes in multiple sequences of different importance, we first define a new kind of episodes, i.e., the weighted frequent closed episodes, to take sequence importance, episode distribution and occurrence frequency into account together. Secondly, to facilitate the mining of such new episodes, we present a new concept called maximal duration serial episodes to cut a whole sequence into multiple maximum episodes using duration constraints, and discuss its properties for episode shrinking processing. Finally, based on the theoretical properties, we propose a two-phase approach to efficiently mine these new episodes. In Phase I, we adopt a level-wise episode shrinking framework to discover the candidate frequent closed episodes with the same prefixes, and in Phase II, we match the candidates with different prefixes to find the frequent close episodes. Experiments on simulated and real datasets demonstrate that the proposed episode mining strategy has good mining effectiveness and efficiency.

show abstract