Retraction Note to: Normal discriminant deep convolution neural classification based web behavioral pattern mining for user identification

Gokulapriya, R.; Kumar, Rahul

doi:10.1007/s12652-022-03967-x

Cited by 1 publication

(1 citation statement)

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“…Frequent pattern (FP) mining (Han et al, 2000, 2004; Margret et al, 2021) is one of the most important tasks in data mining (DM; Agrawal & Srikant, 1994; Aryabarzan et al, 2018; Fournier‐Viger et al, 2017). Since the Apriori algorithm (Agrawal & Srikant, 1994) was proposed in 1994, many methods for efficiently mining FPs have been developed, such as FP‐tree‐based methods (Han et al, 2000), Itemset‐Tidset (IT)‐tree‐based methods (Deng, 2016; Vo et al, 2012), hybrid methods (H. Bui et al, 2018; Jamsheela & Raju, 2021; T. Le & Vo, 2015; Vo et al, 2017), and applications of FPs (Gokulapriya & Kumar, 2021; L. Li et al, 2021). Frequent subgraph mining (FSM; Abdelhamid et al, 2016; Elseidy et al, 2014; N.‐T.…”

Section: Introductionmentioning

confidence: 99%

Subgraph mining in a large graph: A review

Nguyen

Zelinka

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et al. 2022

WIREs Data Min & Knowl

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Large graphs are often used to simulate and model complex systems in various research and application fields. Because of its importance, frequent subgraph mining (FSM) in single large graphs is a vital issue, and recently, it has attracted numerous researchers, and played an important role in various tasks for both research and application purposes. FSM is aimed at finding all subgraphs whose number of appearances in a large graph is greater than or equal to a given frequency threshold. In most recent applications, the underlying graphs are very large, such as social networks, and therefore algorithms for FSM from a single large graph have been rapidly developed, but all of them have NP-hard (nondeterministic polynomial time) complexity with huge search spaces, and therefore still need a lot of time and memory to restore and process. In this article, we present an overview of problems of FSM, important phases in FSM, main groups of FSM, as well as surveying many modern applied algorithms. This includes many practical applications and is a fundamental premise for many studies in the future.

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