Research on Alarm Causality Filtering Based on Association Mining

2021 ◽  
pp. 452-458
Author(s):  
Yuan Liu ◽  
Yi Man ◽  
Jianuo Cui
Keyword(s):  
Association Mining

Improved Diagnosis-Medication Association Mining to Reduce Pseudo-associations

2021 ◽  
pp. 106181
Author(s):  
Ching-Huan Wang ◽  
Phung Anh Nguyen ◽  
Yu Chuan (Jack) Li ◽  
Md. Mohaimenul Islam ◽  
Tahmina Nasrin Poly ◽  
...  
Keyword(s):  
Association Mining

Urban community structure detection based on the OD of traffic analysis zones

2019 ◽  
Vol 33 (13) ◽  
pp. 1950164
Author(s):  
Qing-Feng Dong ◽  
Dian-Kun Chen ◽  
Ting Wang
Keyword(s):  
Community Structure ◽  
Quantitative Methods ◽  
Urban Community ◽  
Spatial Location ◽  
Traffic Analysis ◽  
Association Mining ◽  
Community Structures ◽  
City Community ◽  
Traffic Characteristics ◽  
Structure Detection

At present, the detection of urban community structures is mainly based on existing administrative divisions, and is performed using qualitative methods. The lack of quantitative methods makes it difficult to judge the rationality of urban community divisions. In this study, we used complex network association mining methods to detect a city community structure by using the Origin-Destinations (OD) at traffic analysis zone (TAZ) level, and successively assigned all the TAZs into different communities. Based on the community results, we calculated the community core degree of each TAZ within every community, and then calculated the Traffic Core Degree and Location Core Degree indicators of the community based on OD passenger flow and spatial location relationship between communities. Finally, we analyzed the correlation among three indicators to ensure the rationality of the community structure. We used the city of Zhengzhou in 2016 as an example case study. For Zhengzhou, we detected a total of six communities. We found a relatively low correlation between Traffic Core Degree and Location Core Degree. Within each group, the correlation between community core degree and Traffic Core Degree was higher than that between community core degree and Location Core Degree, indicating that the urban community structure is more reasonably based on traffic characteristics. The development of a quantitative approach for determining reasonable city community structures has important implications for transportation planning and industrial layout.

Drug–target prediction utilizing heterogeneous bio-linked network embeddings

2019 ◽  
Author(s):  
Nansu Zong ◽  
Rachael Sze Nga Wong ◽  
Yue Yu ◽  
Andrew Wen ◽  
Ming Huang ◽  
...  
Keyword(s):  
Drug Target ◽  
Target Prediction ◽  
Machine Learning Algorithms ◽  
Association Mining ◽  
Drug Target Prediction ◽  
Specific Prediction ◽  
Series Of Experiments ◽  
Inference Methods ◽  
Novel Drug ◽  
Prediction Strategy

Abstract To enable modularization for network-based prediction, we conducted a review of known methods conducting the various subtasks corresponding to the creation of a drug–target prediction framework and associated benchmarking to determine the highest-performing approaches. Accordingly, our contributions are as follows: (i) from a network perspective, we benchmarked the association-mining performance of 32 distinct subnetwork permutations, arranging based on a comprehensive heterogeneous biomedical network derived from 12 repositories; (ii) from a methodological perspective, we identified the best prediction strategy based on a review of combinations of the components with off-the-shelf classification, inference methods and graph embedding methods. Our benchmarking strategy consisted of two series of experiments, totaling six distinct tasks from the two perspectives, to determine the best prediction. We demonstrated that the proposed method outperformed the existing network-based methods as well as how combinatorial networks and methodologies can influence the prediction. In addition, we conducted disease-specific prediction tasks for 20 distinct diseases and showed the reliability of the strategy in predicting 75 novel drug–target associations as shown by a validation utilizing DrugBank 5.1.0. In particular, we revealed a connection of the network topology with the biological explanations for predicting the diseases, ‘Asthma’ ‘Hypertension’, and ‘Dementia’. The results of our benchmarking produced knowledge on a network-based prediction framework with the modularization of the feature selection and association prediction, which can be easily adapted and extended to other feature sources or machine learning algorithms as well as a performed baseline to comprehensively evaluate the utility of incorporating varying data sources.

Causal Association Mining from Geriatric Literature

2014 ◽  
Author(s):  
Anand Krishnan ◽  
Jon Sligh ◽  
Eric Tinsley ◽  
Natalie Crohn ◽  
Jean Bandos ◽  
...  
Keyword(s):  
Association Mining ◽  
Causal Association

Educational Association Mining on the Use of Media Platforms for e- Learning

2021 ◽  
Author(s):  
Aparna Agarwal ◽  
Raza Hasan ◽  
Vikas Rao Naidu ◽  
Mohammed Saqib ◽  
Shyamala Srinivas ◽  
...  
Keyword(s):  
Association Mining ◽  
E Learning
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