The Analysis of Internet Commercial Judicial Based on Big Data Alliance and Mining Service Process Model

ZhonglongZhao,; HongliangWang,

doi:10.1155/2021/2897823

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…The majority of Zhao's research on big data mining, feature selection, clustering, and granular computing, which can be used to process big data from social networks using text semantic processing, clustering, and other mining techniques (8) . Wan et al use particular scenarios to demonstrate the usefulness of the suggested approach.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Sequential Pattern Neural Classifier in E-Commerce Data Behavioral Characteristic Extraction

Anima¹,

Aneeshkumar²

2023

IJST

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Objectives: To present a framework for sequential pattern analysis using deep learning with behavioral characteristic extraction. This work intends to address the two major problems of accuracy and false positives predictions due to higher self-similarity in historical clicks. Methods: It implements sequenceaware recommenders for product recommendation using hybrid historical sequential pattern recommendation system (HSPRec) and hybrid sequential pattern based neural (HSPN) algorithm to take advantage of this crucial attribute. Data is gathered from Amazon, Flipkart, and other e-commerce sites. The simulation is carried out in Matlab. Findings: The proposed deep learning model provides 98% accuracy across 46 epochs, which is at least 8% higher, compared to existing works. The false positives in proposed solution are at least 6% lower compared to existing works. Novelty: The accuracy attained in recognizing consumer sequential patterns using HSPN is 95% and 98%, respectively. The computational effectiveness and viability of the neural network algorithm have been shown via our testing of the method.

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Section: Introductionmentioning

confidence: 99%

Implementation of Sequential Pattern Neural Classifier in E-Commerce Data Behavioral Characteristic Extraction

Anima¹,

Aneeshkumar²

2023

IJST

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Big Data Mining and Analysis of Agricultural Products Based on e-Commerce Platform

Meiling

2022

Wireless Communications and Mobile Computing

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Solve the problem of agricultural product big data mining based on e-commerce platform, meet the needs of e-commerce development to agricultural products, meet the diversified needs of e-commerce platforms, and improve people’s living standards and convenience. According to 1000 online questionnaires, 866 people believe that e-commerce can bring them convenience, and 134 people believe that the convenience is insufficient. Even agricultural products, as a traditional primary industry, have begun to be “involved” in the sales mode of e-commerce platforms. In the face of the increasingly huge online consumer demand market, the agricultural product economy has redisplayed a strong market vitality. Of course, the huge market base also makes the e-commerce model of agricultural products pay attention to big data mining and analysis. This paper focuses on how to carry out big data mining and analysis of agricultural products more efficiently from the technical level. Therefore, the agricultural product user data mining technology of e-commerce platform based on Hadoop is proposed. Through the intervention of association rule analysis and algorithm, the improvement of relevant algorithms and agricultural products user behavior analysis system under e-commerce platform based on Hadoop is proposed. The results show that the system can realize the analysis of commodity association degree under various agricultural products user behavior modes and can better help the e-commerce platform of agricultural products realize precision marketing.

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An Exploration of the Computer Big Data Mining Service Model Under Resource Sharing

Hu,

Sun,

2024

Information Resources Management Journal

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In order to meet the diverse needs of users for data mining services and improve resource utilization and enterprise competitiveness, this article aims to construct a Big Data Analytics (BDA) data mining service model based on resource sharing mechanisms. This article designs a customized data mining service model for BDA based on its characteristics. In this model, the authors apply the improved Apriori algorithm to determine the optimization plan and improve the ant colony optimization algorithm to improve the efficiency and accuracy of data mining. By analyzing the experimental results, the scientificity and rationality of the proposed data mining service model for BDA were demonstrated, and the implementation strategy of the data mining model was improved. These research findings provide important references for BDA's data mining service model based on response surface modeling and also provide guidance for enterprises on how to better utilize resources and improve competitiveness when facing big data.

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The Analysis of Internet Commercial Judicial Based on Big Data Alliance and Mining Service Process Model

Cited by 3 publications

References 18 publications

Implementation of Sequential Pattern Neural Classifier in E-Commerce Data Behavioral Characteristic Extraction

Implementation of Sequential Pattern Neural Classifier in E-Commerce Data Behavioral Characteristic Extraction

Big Data Mining and Analysis of Agricultural Products Based on e-Commerce Platform

An Exploration of the Computer Big Data Mining Service Model Under Resource Sharing

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