Research on knowledge mining for agricultural machinery maintenance based on association rules

Hu, Yaoguang; Guo, Zhengjie; Wen, Jinming; Han, Junqiang

doi:10.1109/iciea.2015.7334235

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…For, the author has used general topology of big data, it can't apply to real time applications. Yaoguang Hu et al [8] presented knowledge mining systems for agricultural machinery maintenance systems. Apriori algorithm was used for the framing rule which throws higher complexity due to the combination of multiple variables.…”

Section: Prior Workmentioning

confidence: 99%

Predicting Instructors Performance in Higher Education Systems

Kalaiselvi¹,

Sowmiya²

2018

EAI Endorsed Transactions on Energy Web

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In recent years, knowledge mining has become one of the effective tools for data analysis and information management systems. Educational sector is the recent research endeavors that make use of data mining algorithms. Prior works carried in data mining algorithms like J48 Decision Tree, Multilayer Perception, Naïve Bayes, and Sequential Minimal Optimization impose issues like the curse of dimensionality, cardinality and imbalance attributes. In this paper, we have proposed FA-Paired t-test method which is a novel knowledge discovery process to predict the performance of the instructors. The aim of the study is to find the factors that associated for the prediction of teaching quality. Thus, the contribution of the factor analysis method helps to find the relevant attributes from a set of attributes. Then, the selected attributes are fed as input to paired t-test model which find the associations between those linked attributes. The selected attributes are experimenting using SPSS modeler. Many attributes test for the performance evaluation. It is strongly found that content arrangement, delivery of speech, effective class hours and completion of the course helps to predict the quality of the teaching. In addition to, the proposed model is compared to prior two classifiers, named, J48DT and Naïve Bayes which shows our proposed method works better than other two classifiers in term of Attribute reduction and evaluation process.

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Section: Prior Workmentioning

confidence: 99%