2020
DOI: 10.1109/access.2020.3019480
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A Novel Framework for Recommending Data Mining Algorithm in Dynamic IoT Environment

Abstract: Internet of Things (IoT) has been the driving force for many smart city applications. The huge volume of IoT data generated from these applications require efficient processing to get the insight, which poses significant difficulty. Data mining and machine learning (DM) algorithms are used to minimize such difficulty. However, it is still very challenging to select a particular DM algorithm that can process a dynamic IoT dataset based on some application-specific goals to achieve better accuracy. This paper pr… Show more

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Cited by 15 publications
(4 citation statements)
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“…Hossain et al made use of DM technology, and they tried to fnd out the interrelation of various factors from the data and found some new laws that were produced with the dynamic changes of various factors to guide the school sports research and teaching training and then found sports talents [15]. Li et al also discussed and studied the knowledge discovery of Web DM and unstructured data [16].…”
Section: Dm Technologymentioning
confidence: 99%
“…Hossain et al made use of DM technology, and they tried to fnd out the interrelation of various factors from the data and found some new laws that were produced with the dynamic changes of various factors to guide the school sports research and teaching training and then found sports talents [15]. Li et al also discussed and studied the knowledge discovery of Web DM and unstructured data [16].…”
Section: Dm Technologymentioning
confidence: 99%
“…Therefore the chances for data attack is reduced in the cloud computing process. A recommendation work on data mining algorithms [15] was structured to find out the best suitable algorithm for analyzing the big data with respect to their applications. The work says that all the data mining algorithms wont suite for all the applications.…”
Section: Related Workmentioning
confidence: 99%
“…Unfortunately, few NRSs have been widely adopted due to reasons such as limited ability to provide personalized recommendations, inadequate suitability of existing algorithms for evolving nutritional needs, low quality and reliability of the data, low involvement of nutrition experts and so on [18][19][20][21][22][23][24] . A few studies have reviewed RSs in the food domain, addressing the technical aspects and research challenges of RSs [25][26][27] .…”
Section: Introductionmentioning
confidence: 99%