Intelligent Data Analysis Using Hadoop Cluster – Inspired MapReduce Framework and Association Rule Mining on Educational Domain

Guleria, Pratiyush; Sood, Manu

doi:10.1002/9781119544487.ch7

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Shelke [48] Analysed the product based on transactional data with association rules mining and provide decision support for product sales and customer consumption. In addition, ARM also can provide intelligent decision-making for other fields, such as agriculture [23][51], garment industry [28], education industry [18][3], medical industry [29][7], transportation [11], etc.…”

Section: The Application Of Association Rules Mining Methods (Arm)mentioning

confidence: 99%

Association mining-based method for enterprise’s technological innovation intelligent decision making under big data

Zhang

Geng²,

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Technological innovation is vital for the survival and development of enterprises. In the era of intelligent information interconnection and knowledge-driven economy, there is a growing interest in how to manage high-volume data, unlock its potential value, and provide intelligent analysis and decision-making support for enterprise’s technological innovation. This paper proposes an improved knowledge association analysis method based on the semantic concept model. This approach enables the discovery of potential correlations and interaction modes between the influencing factors of enterprise’s technological innovation, and provides a useful reference for decision-making by combining the analysis with the enterprise’s own situation.

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Section: The Application Of Association Rules Mining Methods (Arm)mentioning

confidence: 99%

Association mining-based method for enterprise’s technological innovation intelligent decision making under big data

Zhang

Geng²,

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…Some of the direct areas of clustering application generally discussed in the literature have been textual document classification, image segmentation, object recognition, character recognition, information retrieval, data mining, spatial data analysis, business analytics, data reduction, and big data mining. Other areas indicated by Saxena et al ( 2017 ), have been sequence analysis (Durbin et al 1998 ; Li et al 2012 ), human genetic clustering, (Kaplan and Winther 2013 ; Lelieveld et al 2017 ; Marbac et al 2019 ), mobile banking and information system (Motiwalla et al 2019 ; Shiau et al 2019 ), social network analysis (Scott and Carrington 2011 ; Shiau et al 2017 ; Khamparia et al 2020 ), search result grouping (Mehrotra and Kohli 2016 ; Kohli and Mehrotra 2016 ), software evolution (Rathee and Chhabra 2018 ; Izadkhah and Tajgardan 2019 ), recommender systems (Petwal et al 2020 ), educational data mining (Baker 2010 ; Guleria and Sood 2020 ), climatology (Sharghi et al 2018 ; Pike and Lintner 2020 ; Chattopadhyay et al 2020 ) and robotics (Khouja and Booth 1995 ; Zhang et al 2013 ). In Table 6 below we briefly discuss a few applications as indicated by Saxena et al ( 2017 ) and also provide references for more detailed studies.…”

Section: Applications Of Clusteringmentioning

confidence: 99%

Data clustering: application and trends

Oyewole

Thopil

2022

Artif Intell Rev

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Clustering has primarily been used as an analytical technique to group unlabeled data for extracting meaningful information. The fact that no clustering algorithm can solve all clustering problems has resulted in the development of several clustering algorithms with diverse applications. We review data clustering, intending to underscore recent applications in selected industrial sectors and other notable concepts. In this paper, we begin by highlighting clustering components and discussing classification terminologies. Furthermore, specific, and general applications of clustering are discussed. Notable concepts on clustering algorithms, emerging variants, measures of similarities/dissimilarities, issues surrounding clustering optimization, validation and data types are outlined. Suggestions are made to emphasize the continued interest in clustering techniques both by scholars and Industry practitioners. Key findings in this review show the size of data as a classification criterion and as data sizes for clustering become larger and varied, the determination of the optimal number of clusters will require new feature extracting methods, validation indices and clustering techniques. In addition, clustering techniques have found growing use in key industry sectors linked to the sustainable development goals such as manufacturing, transportation and logistics, energy, and healthcare, where the use of clustering is more integrated with other analytical techniques than a stand-alone clustering technique.

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Intelligent Data Analysis Using Hadoop Cluster – Inspired MapReduce Framework and Association Rule Mining on Educational Domain

Cited by 2 publications

References 18 publications

Association mining-based method for enterprise’s technological innovation intelligent decision making under big data

Association mining-based method for enterprise’s technological innovation intelligent decision making under big data

Data clustering: application and trends

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