Association Rule Mining using Apriori for Large and Growing Datasets under Hadoop

Govada, Aruna; Patluri, Abhinav; Honnalgere, Atmika

doi:10.1145/3171592.3171621

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…In addition, Equation (6) shows an equation for calculating the Fmeasure using accuracy and recall. The performance evaluation uses the proposed miningbased mutual information (MbMI), existing mining-based word frequency (MbWF) [37,38], word concurrence frequency (WCoF) [39,40] in the document to find the relationship between words. It performs performance evaluation while repeatedly changing minimum support.…”

Section: B Performance Evaluationmentioning

confidence: 99%

Multi-Level Health Knowledge Mining Process in P2P Edge Network

Baek

Chung

2021

IEEE Access

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Chronic diseases are increasing due to westernized eating habits and everyday life changes, and healthcare and disease prevention should be managed based on constant interest. Users, who are not health professionals, have difficulty in obtaining accurate information related to healthcare due to noise problems such as subjective opinions, distorted information, and exaggerated information. There is a need for a method that enables users to obtain meaningful information for healthcare and disease prevention in real-time among the vast amounts of data collected through search. In this study, we propose a multi-level health knowledge mining process in a P2P edge network. The proposed method suggests a P2P edge network to solve the overload problem of P2P networking, the noise problem, and the security problem of cloud computing and mines the health knowledge through the mutual information according to the association rules. In addition, the results of health knowledge mining are visualized to propose a method by which users can easily receive relevant health information. As a result of the performance evaluation, the F-measure using recall and precision is 83%, 79%, 75%, 74%, and 73% of the support ratings of 10%, 20%, 30%, 40%, and 50% Was derived. Accordingly, it is possible to process and analyze healthcare-related information in realtime through a multi-level based health knowledge tree based on the association of data collected by P2P edge computing. In addition, by visualizing meaningful information to the user through the embedding network structure, it provides personalized information for intuitive understanding.

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