The Study of Association Rule Mining Technology in Hospital Information System Analysis

2013 ◽  
Author(s):  
Shunmin Wang
Keyword(s):  
Information System ◽  
Association Rule ◽  
Hospital Information System ◽  
Association Rule Mining ◽  
System Analysis ◽  
Rule Mining ◽  
Mining Technology

Study on Application of Data Mining Technology in Syndrome Differentiation of TCM

2013 ◽  
Vol 765-767 ◽  
pp. 282-285
Author(s):  
Zhi Guo Dai ◽  
Yang Yang Han
Keyword(s):  
Data Mining ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Syndrome Differentiation ◽  
Rule Mining ◽  
Mining Technology ◽  
Disease Symptoms

Study on the applications of association rule mining in traditional Chinese medicine (TCM) knowledge and experience is carried out in this paper. The association rules of disease symptoms and syndrome differentiation, syndrome differentiation and prescription, disease symptoms and prescription are mined by analyzing the cases of patients with chronic gastritis, and then the mined association rules are interpreted that provide the beneficial reference for data mining technology in TCM.

Image Classification and Retrieval with Mining Technologies

2010 ◽  
pp. 96-110 ◽  
Author(s):  
Yu-Jin Zhang
Keyword(s):  
Image Classification ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Mining Technology ◽  
New Approach ◽  
Retrieval Error ◽  
Computation Efficiency ◽  
Novel Method ◽  
Two Sides

Mining techniques can play an important role in automatic image classification and content-based retrieval. A novel method for image classification based on feature element through association rule mining is presented in this chapter. The effectiveness of this method comes from two sides. The visual meanings of images can be well captured by discrete feature elements. The associations between the description features and the image contents can be properly discovered with mining technology. Experiments with real images show that the new approach provides not only lower classification and retrieval error but also higher computation efficiency.

scholarly journals Analysis of Run-Off-Road Accidents by Association Rule Mining and Geographic Information System Techniques on Imbalanced Datasets

2020 ◽  
Vol 12 (12) ◽  
pp. 4882 ◽  
Author(s):  
Feifeng Jiang ◽  
Kwok Kit Richard Yuen ◽  
Eric Wai Ming Lee ◽  
Jun Ma
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Spatial Analysis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Geographic Information ◽  
Rule Mining ◽  
Imbalanced Datasets ◽  
Fatal Accidents ◽  
Run Off

Run-off-road (ROR) accidents cause a large proportion of fatalities on roads. Exploring key factors is an effective method to reduce fatalities and improve safety sustainability. However, some limitations exist in current studies: (1) Datasets of ROR accidents have imbalance problems, in which the samples of fatal accidents (FA) are much less than non-fatal accidents (NFA). Data mining methods on such imbalanced datasets make the results biased. (2) Few studies conducted spatial analysis of ROR accidents in visualization. Therefore, this study proposes an association rule mining (ARM)-based framework to analyze ROR accidents on imbalanced datasets. A novel method is proposed to address the imbalance problem and ARM is applied to analyze accident severity. Geographic information system (GIS) is adopted for spatial analysis of ROR accidents. The proposed framework is applied to ROR accidents in Victoria, Australia. Six FA factors and seven NFA factors are identified from two-item rules. The results of three-item rules indicate factors acting interactively increase the likelihood of FA or NFA. Hot spots of ROR accidents are presented by GIS maps. Effective measures are accordingly proposed to improve road safety. Compared with traditional data-balancing methods, the proposed framework has been validated to provide more robust and reliable results on imbalanced datasets.

scholarly journals An improved association rule mining algorithm for large data

2021 ◽  
Vol 30 (1) ◽  
pp. 750-762
Author(s):  
Zhenyi Zhao ◽  
Zhou Jian ◽  
Gurjot Singh Gaba ◽  
Roobaea Alroobaea ◽  
Mehedi Masud ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rapid Development ◽  
Large Data ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Mining Technology ◽  
Database Technology

Abstract The data with the advancement of information technology are increasing on daily basis. The data mining technique has been applied to various fields. The complexity and execution time are the major factors viewed in existing data mining techniques. With the rapid development of database technology, many data storage increases, and data mining technology has become more and more important and expanded to various fields in recent years. Association rule mining is the most active research technique of data mining. Data mining technology is used for potentially useful information extraction and knowledge from big data sets. The results demonstrate that the precision ratio of the presented technique is high comparable to other existing techniques with the same recall rate, i.e., the R-tree algorithm. The proposed technique by the mining effectively controls the noise data, and the precision rate is also kept very high, which indicates the highest accuracy of the technique. This article makes a systematic and detailed analysis of data mining technology by using the Apriori algorithm.

Application of Association Rule Mining on Food Safety Test Data

2014 ◽  
Vol 556-562 ◽  
pp. 4681-4684
Author(s):  
Kai Chen ◽  
Hong Tan ◽  
Jie Gao ◽  
Da Xia Wang
Keyword(s):  
Information System ◽  
Food Safety ◽  
Test Data ◽  
Association Rule ◽  
Association Rule Mining ◽  
Analytical Models ◽  
Rule Mining ◽  
Safety Test ◽  
Testing Data

For the purpose of improving scientific regulatory and effectively making use of supervision information for food safety, we have collected more than 200,000 testing data in the information system from 2009 to 2013. In this paper, with association rule, several analytical models, as exemplified by soy data, was created according to the feature of soy. Valuable information can be obtained from massive testing data, which is conducive to food safety supervisions.

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