Mining Surgery Phase-Related Sequential Rules from Vertebroplasty Simulations Traces

Toussaint, Ben-Manson; Luengo, Vanda

doi:10.1007/978-3-319-19551-3_5

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Castillo [106] suggested the PM use in the context of a virtual laboratory for a multiagent programming course to detect successful and failed behaviors of students. Another simulation-based environment TELEOS (Technology Enhanced Learning Environment for Orthopedic Surgery) served as a data source in [107]. The extraction of sequential rules common to several sequences was used to mine the distinctive phases of simulated operations of vertebroplasty.…”

Section: Educational Virtual Environmentsmentioning

confidence: 99%

Reviewing Process Mining Applications and Techniques in Education

Sypsas¹,

Kalles²

2022

IJAIA

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Process Mining (PM) emerged from business process management but has recently been applied to educational data and has been found to facilitate the understanding of the educational process. Educational Process Mining (EPM) bridges the gap between process analysis and data analysis, based on the techniques of model discovery, conformance checking and extension of existing process models. We present a systematic review of the recent and current status of research in the EPM domain, focusing on application domains, techniques, tools and models, to highlight the use of EPM in comprehending and improving educational processes.

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Section: Educational Virtual Environmentsmentioning

confidence: 99%

Reviewing Process Mining Applications and Techniques in Education

Sypsas¹,

Kalles²

2022

IJAIA

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“…Mining the sequential rules is an alternative to sequential patterns that assist the users in having knowledge of sequence items for making a prediction. It has been found in numerous zones like electronic learning [5], manufacturing simulation [6], the analysis of customer behavior [7], and decision systems [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation of Sequential Rule Mining Algorithms

Abdelwahab

Youssef

2022

Applied Sciences

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Data mining techniques are useful in discovering hidden knowledge from large databases. One of its common techniques is sequential rule mining. A sequential rule (SR) helps in finding all sequential rules that achieved support and confidence threshold for help in prediction. It is an alternative to sequential pattern mining in that it takes the probability of the following patterns into account. In this paper, we address the preferable utilization of sequential rule mining algorithms by applying them to databases with different features for improving the efficiency in different fields of application. The three compared algorithms are the TRuleGrowth algorithm, which is an extension sequential rule algorithm of RuleGrowth; the top-k non-redundant sequential rules algorithm (TNS); and a non-redundant dynamic bit vector (NRD-DBV). The analysis compares the three algorithms regarding the run time, the number of produced rules, and the used memory to nominate which of them is best suited in prediction. Additionally, it explores the most suitable applications for each algorithm to improve the efficiency. The experimental results proved that the performance of the algorithms appears related to the dataset characteristics. It has been demonstrated that altering the window size constraint, determining the number of created rules, or changing the value of the minSup threshold can reduce execution time and control the number of valid rules generated.

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