Analysis of tree-based uncertain frequent pattern mining techniques without pattern losses

Lee, Gangin; Yun, Unil; Lee, Kyung-Min

doi:10.1007/s11227-016-1847-z

Cited by 1 publication

(2 citation statements)

References 31 publications

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“…The method extracts the commodities with large values that can help to find the non-defective products in the manufacturing plants [33]. Lee et al [34] proposed a tree structure based on the uncertain frequent pattern mining approach. The method allows to examine the uncertain data and overcome the limitations of traditional approaches that is not able to concern the probabilities of items for patternmining progress [34].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Lee et al [34] proposed a tree structure based on the uncertain frequent pattern mining approach. The method allows to examine the uncertain data and overcome the limitations of traditional approaches that is not able to concern the probabilities of items for patternmining progress [34]. Lin et al [35] addressed the uncertain HUIM by considering the existence of probabilistic of the transactions based on a list structure.…”

Section: Literature Reviewmentioning

confidence: 99%

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An Evolutionary Model to Mine High Expected Utility Patterns From Uncertain Databases

Ahmed

Lin

Srivastava

et al. 2021

IEEE Trans. Emerg. Top. Comput. Intell.

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In recent decades, mobile or the Internet of Thing (IoT) devices are dramatically increasing in many domains and applications. Thus, a massive amount of data is generated and produced. Those collected data contain a large amount of interesting information (i.e., interestingness, weight, frequency, or uncertainty), and most of the existing and generic algorithms in pattern mining only consider the single object and precise data to discover the required information. Meanwhile, since the collected information is huge, and it is necessary to discover meaningful and up-to-date information in a limit and particular time. In this paper, we consider both utility and uncertainty as the majority objects to efficiently mine the interesting high expected utility patterns (HEUPs) in a limit time based on the multiobjective evolutionary framework. The benefits of the designed model (called MOEA-HEUPM) can discover the valuable HE-UPs without pre-defined threshold values (i.e., minimum utility and minimum uncertainty) in the uncertain environment. Two encoding methodologies are also considered in the developed MOEA-HEUPM to show its effectiveness. Based on the developed MOEA-HEUPM model, the set of non-dominated HEUPs can be discovered in a limit time for decision-making. Experiments are then conducted to show the effectiveness and efficiency of the designed MOEA-HEUPM model in terms of convergence, hypervolume and number of the discovered patterns compared to the generic approaches.

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Section: Literature Reviewmentioning

confidence: 99%