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The social media has become an integral part of our daily life. The social web users interact and thus influence each other influence in many aspects. Blogging is one of the most important features of the social web. The bloggers share their views, opinions and ideas in the form of blog posts. The influential bloggers are the leading bloggers who i...
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... TN or True Negative is a condition when the model predicts that the user is not active and user is not active the false negative or FN is a condition when the model predict that the user is not active and it is active. The table 2 shows an example of all these classes. Capital letters indicates user P1-4 corresponds to different posts. ...Similar publications
The social Web is a widely used platform for online discussion. Across social media, users can start discussions by posting a topical image, url, or message. Upon seeing this initial post, other users may add their own comments to the post, or to another user's comment. The resulting online discourse produces a comment thread, which constitutes an...
In the era of the Social Web, there has been explosive growth of user-generated content published on various online web forums. Segments of short texts have become a fashionable writing format because they are convenient to post and respond. Examples include comments, tweets, reviews, questions/answers, to name a few. Given the large volume of shor...
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... 3 Comprehensive Integration Weighting Method The comprehensive integration weighting method integrates the weights calculated by the subjective weighting method and the objective weighting method through a certain equation. The integrated results reflect the subjective intention of the evaluator and the objectivity of the evaluation data at the same time, avoiding the defect that the subjective weight is too subjective and the objective weight lacks subjective information [23][24][25]. ...
Using the RFM (Recency, Frequency, Monetary value) model can provide valuable insights about customer clusters which is the core of customer relationship management. Due to accurate customer segment coming from dynamic weighted applications, in-depth targeted marketing may also use type of dynamic weight of R, F and M as factors. In this paper, we present our dynamic weighted RFM approach which is intended to improve the performance of customer segmentation by using the factors and variations to attain dynamic weights. Our dynamic weight approach is a kind of Custom method in essential which roots in the understanding of the data set. Firstly, Analytic Hierarchy Process is used to calculate the subjective weight, then the entropy method is applied to calculate the objective weight. Finally, we use comprehensive integration weighting method to combine the subjective and objective weight to obtain the final weight of the index to calculate the individual user value and quantify the user value difference. The experiment shows that the dynamic weight we used in RFM model is vital, affects the customer segmentation performance positively. Also, this study indicates that customer segments containing dynamic weighted RFM scores bring about stronger and more accurate association rules for the understanding of customer behavior. At last, we discuss the limitations of RFM analysis.
... They compared their results to the results from degree centrality and PageRank centrality. Additionally, authors in [15] applied association rule learning to find influential bloggers using the Apriori algorithm combined with the Oracle data miner. This was also done to find frequent patterns among bloggers holding blog activities together and discover who influences others based on the rules learned from association rule mining. ...
... It is necessary to constrain the mining work in advance, and the content of the constraint must ensure that the mining work has a small amount of calculation and a strong effect [17,18]. ...
The current marketing data management method cannot analyze and mine the correlation between marketing data, which leads to the confusion of the attributes of marketing data mining results and the inability to realize efficient data scheduling. This paper introduces improved association rules into precision marketing data mining of e-commerce platform. Optimize the background hardware of the e-commerce platform, collect and analyze the multi-source data. Information fusion method is used to optimize association rules, and combined with distributed similarity, accurate mining of marketing data of e-commerce platform is completed. Experimental results show that the proposed method can realize precision marketing data mining and has high robustness.
... In a study [4,26], it is proposed to apply the training of association rules to find influential bloggers in time using the Apriori algorithm. An improved Apriori algorithm has been proposed to identify the relationship between the TECP and the thirty-five factors, which cover four categories of household characteristics, including housing, socio-demographic, household appliances and heating, and energy attitudes [9]. ...
... In this strategy, the original data is regarded as the bottom data of mining database, and the index item of practical ability is regarded as the middle layer. The original data is summarized to the corresponding middle layer through coding technology, and finally, the association rules between the target layer and the middle layer are found [10]. The flow chart of association rule mining algorithm is shown in Figure 1. ...
The current evaluation method does not consider the coordination and overall situation of various practical skills, which affects the evaluation contact degree and leads to hidden dangers of production safety. Therefore, an evaluation method of chemical technology safety practical operation ability based on a stochastic model is proposed. Using association rules to design the feature mining algorithm of practical operation ability, combined with crosslayer coding strategy, the mapping relationship between safety accidents and practical operation ability is established. The chemical safety practical operation skills are simulated by a random model, and the practical operation skills are classified and coordinated according to the simulation predictive control results. According to the coordination results, the evaluation index system is constructed, the index weight is determined, and the evaluation model of chemical technology safety operation ability is constructed. The experimental results show that the maximum connection degree of the evaluation method designed in this paper is 88.06%, and the evaluation results are more accurate, which is helpful to improve the production safety of chemical enterprises.
... 2015; Pastorek et al., 2019;Bhandari et al., 2015;Dong et al., 2021;Cai et al., 2021a;Zhai and Xu, 2020;Bi et al., 2020;Zhou et al., 2020;Tian et al., 2020;Huang et al., 2020;Uma and Alias, 2020;Kalaivaani and Thangarajan, 2020;Kim et al., 2020a). Moreover, as environmental issues Huang et al., 2021a,b;Huang and Wang, 2021) have always been considered an essentially vital issue, it is suggested to have a less hazardous impact during oil production (Iravani et al., 2018;Noshi and Schubert, 2018;Amott, 1959;Shazad et al., 2020;Kim et al., 2020b;Tümer and Akkus, 2019;Xue et al., 2019;Xu, 2019;Zhang, 2019;Zhu and Yang, 2019;; ...
One of the challenging conditions in wellbore management is high-pressure, high-temperature wells that apply large expenditures and maintenance costs to petroleum industries. In this regard, drilling fluid rheology and its crucial factors would help engineers to have a fundamental understanding of the most appropriate wellbore management. Moreover, it can help engineers to control the fluid loss phenomenon. This paper aimed to consider different artificial intelligence and machine learning models to predict the drilling fluid density and select the optimum model, which can be extended to field applications. These models are ANFIS (adaptive neuro-fuzzy inference system), PSO-ANFIS (particle swarm optimization-adaptive neuro-fuzzy inference system), LSSVM-GA (least square support vector machine-genetics algorithm), and RBF (radial basis function) algorithm were modeled in the programming era. In LSSVM-GA model, it is evident that the proper linear equation for the prediction of drilling fluid density is ''y = 1.0041x + 0.0019'' with the correlation factor of (R 2 = 0.9966). Due to the advantages of RBF algorithm to other genetics algorithm, this algorithm was used in this part to predict the drilling fluid density. Therefore, it is evident that the proper linear equation for the prediction of drilling fluid density is ''y = 1.0009x + 0.0034'' with the correlation factor of (R 2 = 0.9999). Consequently, RBF model was selected as the optimum method in drilling fluid prediction. Furthermore, there is a proper match with training and experimental data and the maximum deviation is about-0.006. In ANFIS algorithm, exponential derivation would be preferred to predict the drilling fluid density instead of linear equation. Consequently, the RBF model provide has a good agreement with the experimental drilling fluid density. It is indicated that the model have appropriate accuracy and validity with experimental data.
... For example, in the IoT system, it is important to discover and analyze key parts that are easily damaged. Mining communities or nodes play an important role in the transmission of information in virtual communities [7,8], and research and analysis are conducted on their behaviors. In addition, community detection can be applied to the knowledge graph [9,10] to mine abnormal data [11,12], which can lay the foundation for building a knowledge graph or big data mining providing services. ...
... After the algorithm passes through stage 1, the main community has formed. However, the quality of the division is not good enough, and there are too many communities, so the communities need to be further divided through algorithm stage 2. In algorithm stage 2, first compress the result of stage 1 division, and rebuild a new network G ′ , and then the LSCR value of each node according to Algorithm 1, and then the seed nodes are selected by formula (8), and its set is ...
... Step 3: compress each community obtained in stage 1 into a super point, and construct a new network graph G′, and then obtain the LSCR value of the node according to Algorithm 1 in G′, and then select the seed node according to formula (8). ...
According to the needs of scientific research project research and development, the research of cooperative team excavation methods was carried out. Aiming at the current difficulties in accurately and reliably defining and identifying cooperative research teams from co-author network, an improved Louvain algorithm that integrates core node recognition was proposed: Louvain-LSCR algorithm. Based on the analysis of the Louvain algorithm, considering the local topology of the node in the network and the communication range of the node, a new algorithm LSCR for core node identification was constructed. The LSCR algorithm and Louvain algorithm were merged to obtain a new and improved algorithm, Louvain-LSCR. In this algorithm, the leaf nodes in phase 1 of Louvain algorithm were first pruned to reduce calculations; then, seed nodes were selected according to the LSCR algorithm in phase 2. The experimental results on related datasets show that LSCR algorithm has certain advantages in identifying core nodes. The modularity of Louvian-LSCR algorithm is better than other algorithms, and the community structure is more reasonable. It was verified that the algorithm can mine potential cooperative research teams in co-author network.
... At present, the Apriori algorithm has already been successfully applied in business, network security, mobile communication, and other similar studies [22,23]. The operation and maintenance process of the telecom platform involves many links, complex information, and diverse types, and the correlation between different types and departments of information transmission is obvious [24]. The mining and detection of the associated information in the operation and maintenance of the telecom platform improve the efficiency and quality of the business platform maintenance and solve the collaboration between the platform operation and sale, and it has become an urgent issue to be solved in the telecom industry. ...
The operation and maintenance management of telecom platforms puts more emphasis on operation and alarm monitoring and less on business flow, and it is difficult to have a seamless connection between operation and maintenance and sales. To improve the cooperative efficiency and the informatization level of telecom platform operation and maintenance, a new operation and maintenance system integrating Apriori association rules for a telecom platform is proposed. In this paper, the Apriori algorithm is firstly used to analyse the fault correlation of the operation and maintenance system of the telecommunications platform to get the alarm message. Then, the fault risk of the operation and maintenance platform is evaluated intelligently by the system-business alarm causality model. Finally, based on the fixed end and the mobile end, the dynamic presentation of the system running state and alarm monitoring, device query and positioning, and online and offline inspection are implemented and designed, respectively. Experimental results show that the proposed operation and maintenance systems integrating Apriori association rules for the telecom platform innovate the cooperative mode between the operations and sales team, and the average time reduces to 3.9 minutes from 4.7 minutes and the risk forecasting accuracy increases from 8% to 26%. The assumed method can significantly improve the operational efficiency and intelligent level of the telecom platform’s system and business.
The potential relationship between service demands and remanufacturing services (RMS) is essential to make the decision of a RMS plan accurately and improve the efficiency and benefit. In the traditional association rule mining methods, a large number of candidate sets affect the mining efficiency, and the results are not easy for customers to understand. Therefore, a mining method based on binary particle swarm optimization ant colony algorithm to discover service demands and remanufacture services association rules is proposed. This method preprocesses the RMS records, converts them into a binary matrix, and uses the improved ant colony algorithm to mine the maximum frequent itemset. Because the particle swarm algorithm determines the initial pheromone concentration of the ant colony, it avoids the blindness of the ant colony, effectively enhances the searchability of the algorithm, and makes association rule mining faster and more accurate. Finally, a set of historical RMS record data of straightening machine is used to test the validity and feasibility of this method by extracting valid association rules to guide the design of RMS scheme for straightening machine parts.
