An improved Apriori–based algorithm for friends recommendation in microblog

Liu, Li; Yu, Shuo; Wang, Xiang; Ning, Zhaolong

doi:10.1002/dac.3453

Cited by 27 publications

(13 citation statements)

References 30 publications

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“…Candidate generation is implemented using the apriori-gen function [10]. (3) In order to count the support of candidate items, the algorithm needs to scan the data set again.…”

Section: Association Analysis For Role Miningmentioning

confidence: 99%

Big Data Platform Access Control Rule Generation Method Based on Data Mining

Meng¹,

Kong²,

Long³

et al. 2019

dtcse

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The access control problem of big data platform is related to personal privacy, corporate interests and national security. In the context of big data platform, the fine-grained permission relationship between users and resources is difficult to be explored through the experience of administrators, and the permission granularity is difficult to be refined. In this paper, we propose an access control rule generation method based on data mining. By selecting appropriate data preprocessing, clustering analysis, association analysis algorithms and improving them, we dig out the normal access behavior rules of users from the user logs and attributes, and generate fine-grained control rules based on these rules, and improve the accuracy through negative feedback regulation. The experiment results verify the effectiveness and practicability of the method which can provide accurate access control for the big data platform.

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“…Candidate generation is implemented using the apriori-gen function [10]. (3) In order to count the support of candidate items, the algorithm needs to scan the data set again.…”

Section: Association Analysis For Role Miningmentioning

confidence: 99%

Big Data Platform Access Control Rule Generation Method Based on Data Mining

Meng¹,

Kong²,

Long³

et al. 2019

dtcse

View full text Add to dashboard Cite

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“…Since its introduction, it has created several opportunities to mine high-utility data [2]. It helps connect people with common interests [3] and provides new opportunities to improve public health and medicine; the prediction of post-liver transplantation survival is one example [4,5]. This technique helps users find documents relevant to their search [8] and potentially leads to beneficial services.…”

Section: Related Workmentioning

confidence: 99%

“…So, for a child with six entries (3, 6, 9, 1, 12, 5), the randomly selected fraction is 50%. Then entries (1,2,3) are checked with the mutation rate; if entry (3) is a hit, it will go to the second step and number (9) should be replaced by another random number selected uniformly by considering the upper and lower limits for this entry.…”

Section: Tracing Examplementioning

confidence: 99%

“…In a set of transactions, ARM focuses on finding associations between frequent items, enabling researchers to predict the frequency of one itemset depending on the frequency of another item in a transaction. The ARM technique has been extensively used in several applications, including privacy preservation [1], market-basket transaction data analysis [2], recommendation [3], health care [4,5], prediction [6], pattern finding in web browsing [7], ranking of text documents [8], and hazard identification [9], among others. The extraction of association rules from transaction datasets was initiated in 1993 [10].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Association Rules Hiding Using Genetic Algorithms

Bux

Wang

et al. 2018

Symmetry

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In today’s world, millions of transactions are connected to online businesses, and the main challenging task is ensuring the privacy of sensitive information. Sensitive association rules hiding (SARH) is an important goal of privacy protection algorithms. Various approaches and algorithms have been developed for sensitive association rules hiding, differentiated according to their hiding performance through utility preservation, prevention of ghost rules, and computational complexity. A meta-heuristic algorithm is a good candidate to solve the problem of SARH due to its selective and parallel search behavior, avoiding local minima capability. This paper proposes simple genetic encoding for SARH. The proposed algorithm formulates an objective function that estimates the effect on nonsensitive rules and offers recursive computation to reduce them. Three benchmark datasets were used for evaluation. The results show an improvement of 81% in execution time, 23% in utility, and 5% in accuracy.

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“…Also, they provide the facilities to enhance the adaption of applications to each user [14]. Recommender systems have been utilized in many fields, like e-commerce [15], health [16], social networks [17,18], industry [19], elearning [20], music [21], Internet of Things (IoT) [22,23], food and nutritional information system [24], and marketing [25]. They produce automation of personalization in the ecommerce environment by employing traditional and modern techniques [26] like machine learning techniques [27].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Study on the Recommender Systems in the E-Commerce

et al. 2020

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Electronic commerce or e-commerce includes the service and good exchange through electronic support like the Internet. It plays a crucial role in today's business and users' experience. Also, e-commerce platforms produce a vast amount of information. So, Recommender Systems (RSs) are a solution to overcome the information overload problem. They provide personalized recommendations to improve user satisfaction. The present article illustrates a comprehensive and Systematic Literature Review (SLR) regarding the papers published in the field of the e-commerce recommender systems. We reviewed the selected papers to identify the gaps and significant issues of the RSs' traditional methods, which guide the researchers to do future work. So, we provided the traditional techniques, challenges, and open issues concerning traditional methods of the field of review based on the selected papers. This review includes five categories of the RSs' algorithms, including Content-Based Filtering (CBF), Collaborative Filtering (CF), Demographic-Based Filtering (DBF), hybrid filtering, and Knowledge-Based Filtering (KBF). Also, the salient points of each selected paper are briefly reported. The publication time of the selected papers ranged from 2008 to 2019. Also, we provided a comparison table of important issues of the selected papers as well as the tables of advantages and disadvantages. Moreover, we provided a comparative table of metrics and review issues for the selected papers. And finally, the conclusions can, to a great extent, provide valuable guidelines for future studies.

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An improved Apriori–based algorithm for friends recommendation in microblog

Cited by 27 publications

References 30 publications

Big Data Platform Access Control Rule Generation Method Based on Data Mining

Big Data Platform Access Control Rule Generation Method Based on Data Mining

Efficient Association Rules Hiding Using Genetic Algorithms

A Systematic Study on the Recommender Systems in the E-Commerce

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