Association Rules Mining Among Interests and Applications for Users on Social Networks

Si, Huayou; Zhou, Jiayong; Chen, Zhihui; Wan, Jian; Xiong, Naixue; Wei, Zhang; Vasilakos, Athanasios V.

doi:10.1109/access.2019.2925819

Cited by 21 publications

(5 citation statements)

References 36 publications

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“…The paper (Si et al. 2019 ) uses Twitter and Linkedin to create a system capable of relating users preferences to the most acceptable job applications for them. All this is possible by using just text mining techniques and association rules.…”

Section: Tasksmentioning

confidence: 99%

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A survey on the use of association rules mining techniques in textual social media

2022

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The incursion of social media in our lives has been much accentuated in the last decade. This has led to a multiplication of data mining tools aimed at obtaining knowledge from these data sources. One of the greatest challenges in this area is to be able to obtain this knowledge without the need for training processes, which requires structured information and pre-labelled datasets. This is where unsupervised data mining techniques come in. These techniques can obtain value from these unstructured and unlabelled data, providing very interesting solutions to enhance the decision-making process. In this paper, we first address the problem of social media mining, as well as the need for unsupervised techniques, in particular association rules, for its treatment. We follow with a broad overview of the applications of association rules in the domain of social media mining, specifically, their application to the problems of mining textual entities, such as tweets. We also focus on the strengths and weaknesses of using association rules for solving different tasks in textual social media. Finally, the paper provides a perspective overview of the challenges that association rules must face in the next decade within the field of social media mining.

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Section: Tasksmentioning

confidence: 99%

“…( 2018 ) Recommendation Web Posts X Si et al. ( 2019 ) Recommendation LinkedIn X X Rao et al. ( 2019 ) Recommendation Twitter X Zheng ( 2020 ) Recommendation Web Page X X Kammergruber et al.…”

Section: Fields Of Applicationmentioning

confidence: 99%

A survey on the use of association rules mining techniques in textual social media

2022

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“…Studies in this field show two significant challenges in generating large items and multiple database transitions in the basic Apriori algorithm. A review of articles in this field shows that, to resolve these two challenges, several ideas have been proposed to improve the basic Apriori algorithm, many of which, while maintaining the overall structure, have added techniques to increase efficiency [19,21,43,44]. is section has tried to cover some ideas proposed in previous studies regarding association rules mining and frequent patterns mining in different applied areas, especially network topologies and distributed environments.…”

Section: Previous Research Studiesmentioning

confidence: 99%

IAMnet: Presentation of Parallelization Approach for Repetitive Mining on Network Topologies with an Improved Apriori Method

Salehpour

Asghari

Javadi

et al. 2022

Wireless Communications and Mobile Computing

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Recently, the discovery of association rules and the consequent mining frequent patterns have attracted the attention of many researchers to discover unknown relationships in big data, especially in networking and distributed environments. In this research, a parallelization-based approach is proposed to improve the performance of the Apriori algorithm in repetitive mining patterns on network topologies. The proposed approach includes two main features: (1) combining centrality criteria of the node and the Apriori algorithm to identify repetitive patterns and (2) using the mapping/reduction method to create parallel processing and achieve optimal values in the shortest time. This approach also pursues three main objectives: reducing the temporal and spatial complexity of the Apriori algorithm, improving the association rules mining process and identifying repetitive patterns, and comparing the proposed approach’s performance on different network topologies to determine the advantages and disadvantages of each topology. Comparing our proposed method and the basic Apriori algorithm, it is concluded that our approach provides acceptable efficiency in terms of evaluation criteria such as energy consumption, network lifetime, and runtime compared to other methods. Experimental results also show that when using our proposed method compared to the basic Apriori algorithm, network life is increased by 7.1%, the runtime is reduced by 43.2%, and the energy consumption is saved by about 41.2%.

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“…As there is rapid growth of information technologies regarding machine learning models, Internet of Things (IoT) [1], and edge and cloud computing [2,3], data-driven mining has become an important topic that can be used to extract the meaningful information from the collections of those techniques. Several pattern mining models [4][5][6][7][8][9] have been extensively studied, and the most fundamental knowledge of pattern mining in knowledge discovery in databases (KDD) is called ARM or association rule mining, which is deployed through varied applications and specific domains. Among them, Apriori was presented for finding the association rules set in transactional databases iteratively.…”

Section: Introductionmentioning

confidence: 99%

Mining Profitable and Concise Patterns in Large‐Scale Internet of Things Environments

Lin

Djenouri

Srivastava

et al. 2021

Wireless Communications and Mobile Computing

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In recent years, HUIM (or a.k.a. high-utility itemset mining) can be seen as investigated in an extensive manner and studied in many applications especially in basket-market analysis and its relevant applications. Since current basket-market scenario also involves IoT equipment to collect information, i.e., sensor or smart devices, it is necessary to consider the mining of HUIs (or a.k.a. high-utility itemsets) in a large-scale database especially with IoT situations. First, a GA-based MapReduce model is presented in this work known as GMR-Miner for mining closed patterns with high utilization in large-scale databases. The k -means model is initially adopted to group transactions regarding their relevant correlation based on the frequency factor. A genetic algorithm (GA) is utilized in the developed MapReduce framework that can be used to explore the potential and possible candidates in a limited time. Also, the developed 3-tier MapReduce model can be easily deployed in Spark for the handlings of any database of large scale for knowledge discovery of closed patterns with high utilization. We created sets of extensive experimental environments for evaluating the results of the developed GMR-Miner compared to the well-known and state-of-the-art CLS-Miner. We present our in-depth results to show that the developed GMR-Miner outperforms CLS-Miner in many criteria, i.e., memory usage, scalability, and runtime.

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Association Rules Mining Among Interests and Applications for Users on Social Networks

Cited by 21 publications

References 36 publications

A survey on the use of association rules mining techniques in textual social media

A survey on the use of association rules mining techniques in textual social media

IAMnet: Presentation of Parallelization Approach for Repetitive Mining on Network Topologies with an Improved Apriori Method

Mining Profitable and Concise Patterns in Large‐Scale Internet of Things Environments

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