Relevance of Data Mining in Digital Library

Mishra, Rosy; Mishra, Aishwarya

doi:10.7763/ijfcc.2013.v2.110

Cited by 4 publications

(2 citation statements)

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“…In ARM technology, the most common method is Apriori, but Apriori needs to scan the database several times during the mining process, which is computationally very expensive. To solve this problem, many researchers have proposed various improvement methods, such as Park-Chen-Yu method (PCY), XML Frequent Pattern Tree (XFP-Tree), Graphical Processing Unit Apriori (GP-Apriori), and Frequent Pattern Growth (FP-Growth), which can effectively reduce the number of database scans in the mining process and improve mining efficiency [8], [9]. ARM has been widely used in different fields, such as fault diagnosis of complex industrial machinery [10], etiological analysis of diseases in medical science [11], [12], stock price prediction in economics [13], etc.…”

Section: Introductionmentioning

confidence: 99%

Discriminating Feed Rate of Combine Harvester by Using Association Rule Mining

Liu

Dai

Tang

et al. 2023

ELEKTRON ELEKTROTECH

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The feed rate is an important evaluation index of combine harvester performance. The quick identification of the amount of feed rate that enters the combine during harvesting is of great significance for the efficiency and operational quality of the combine harvester. To address this issue, this study proposes a feed rate discrimination method based on association rule mining. A self-designed data acquisition system was designed, taking the wheat combine harvester as object, and collected seven speed signals and three torque signals when the feed rate was 6 kg/s~8 kg/s, 8 kg/s~10 kg/s, and 10 kg/s~11 kg/s, respectively. The collected time series data were discretized so as to facilitate the construction of transaction sets. Then, the association rules in the constructed transaction set were mined by FP-Growth, and the rules with weak or no correlation with the increase in feed rate were filtered using min-support, min-confidence, and min-lift of 1.3, 0.8, and 3, respectively, to obtain strong association rules. Then, the strong association rules were constructed as classifiers. The test results showed that the accuracy of the constructed classifier for the identification of 6 kg/s~8 kg/s, 8 kg/s~10 kg/s, and 10 kg/s~11 kg/s feed rates was 100 %, 96 %, and 98.7 %, respectively. Research results can provide a basis for the adjustment of the working state of the combine harvester.

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Section: Introductionmentioning

confidence: 99%

Discriminating Feed Rate of Combine Harvester by Using Association Rule Mining

Liu

Dai

Tang

et al. 2023

ELEKTRON ELEKTROTECH

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“…In general, data mining to manage and understand the information need of users can lead to the discovery of data relationships and the provision of useful information (Suresh et al , 2018). This new and reliable technology with potential functionality in facilitating the tasks of libraries and information centers is an appropriate tool to predict future behavior of users for knowledge-driven decisions (Mishra and Mishra, 2013). So that, data mining can offer related books to users by analyzing lending transactions and lead to improving the quality of library resources and facilitating library management (Yi et al , 2014; Zhang, 2014).…”

Section: Introductionmentioning

confidence: 99%

Using data mining techniques to predict user’s behavior and create recommender systems in the libraries and information centers

Ansari¹,

Vakilimofrad²,

Mansoorizadeh

et al. 2020

GKMC

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Purpose This study aims to analyze and predict a user’s behavior and create recommender systems in libraries and information centers, using data mining techniques. Design/methodology/approach The present study is an analytical survey study of cross-sectional type. The required data for this study were collected from the transactions of the users of libraries and information centers in Hamadan University of Medical Sciences. Using data mining techniques, the existing patterns were investigated, and users’ loan transactions were analyzed. Findings The findings showed that the association rules with the degree of confidence above 0.50 were able to determine user access patterns. Furthermore, among the decision tree algorithms, the C.05 predicted the loan period, referrals and users’ delay with the highest accuracy (i.e. 90.1). The other findings on feedforward neural network with R = 0.99 showed that the predicted results of neural network computation were very close to the real situation and had a proper estimation of user’s delay prediction. Finally, the clustering technique with the k-means algorithm predicted users’ behavior model regarding their loyalty. Practical implications The results of this study can lead to providing effective services and improve the quality of interaction between librarians and users and provide a good opportunity for managers to align supply of information resources with the real needs of users. Originality/value The results of the study showed that various data mining techniques are applicable with high efficiency and accuracy in analyzing library and information centers data and can be used to predict a user’s behavior and create recommendation systems.

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Shaping the Afghan learning settings and power to the learners through a high-tech library management system

Gharanai

2016

2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS)

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Relevance of Data Mining in Digital Library

Cited by 4 publications

References 0 publications

Discriminating Feed Rate of Combine Harvester by Using Association Rule Mining

Discriminating Feed Rate of Combine Harvester by Using Association Rule Mining

Using data mining techniques to predict user’s behavior and create recommender systems in the libraries and information centers

Shaping the Afghan learning settings and power to the learners through a high-tech library management system

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