Results 221 to 230 of about 8,190 (251)

Mining similarity and causality on graphs

2020
Similarity and causality mining on graphs are common and fundamental requirements for graph-based applications. Specifically, the similarities on graphs can be separated into two categories: the similarity between graphs (i.e., graph similarity), and the similarity between nodes (i.e., node similarity) on a graph.
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Mining Causality of Network Events in Log Data

IEEE Transactions on Network and Service Management, 2018
Network log messages (e.g., syslog) are expected to be valuable and useful information to detect unexpected or anomalous behavior in large scale networks. However, because of the huge amount of system log data collected in daily operation, it is not easy to extract pinpoint system failures or to identify their causes. In this paper, we propose a method
Satoru Kobayashi, Kazuki Otomo, Kensuke Fukuda, Hiroshi Esaki   +3 more
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Causal Association Mining from Geriatric Literature

2014 IEEE International Conference on Bioinformatics and Bioengineering, 2014
Literature pertaining to geriatric care contains rich information regarding the best practices related to geriatric health care issues. The publication domain of geriatric care is small as compared to other health related areas, however, there are over a million articles pertaining to different cases and case interventions capturing best practice ...
Anand Krishnan, Jon Sligh, Eric Tinsley, Natalie Crohn, Jean Bandos, Heather Bush, Jason Depasquale, Mathew Palakal   +7 more
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Causal Relations, Text Mining and Causal Graphs

2015
I had the privilege of meetingProf. Trillas in a meeting of the SCIA netgroup organized in Santiago de Compostela (Spain,2005), by Prof. Alejandro Sobrino. I was beginningmy Phd, and although I had heard a lot about Enric, duringmy engineering career and my early Phd, I did not want to meet him as I did not know what to say to such important scientist.
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Mining Causal Knowledge from Diagnostic Knowledge

2008
Diagnostic knowledge is the basis of many non-Bayesian medical systems. To explore the advantages of their Bayesian counterparts, we need causal knowledge. This paper investigates how to mine causal knowledge from the diagnostic knowledge. Experiments indicate the proposed mining method works pretty well.
Xiangdong An 0001, Nick Cercone
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Mining candidate causal relationships in movement patterns

International Journal of Geographical Information Science, 2013
In many applications, the environmental context for and drivers of movement patterns are just as important as the patterns themselves. This article adapts standard data mining techniques, combined with a foundational ontology of causation, with the objective of helping domain experts identify candidate causal relationships between movement patterns and
Susanne Bleisch, Matt Duckham, Antony Galton, Patrick Laube, Jarod Lyon   +4 more
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Mining answers for causal questions in a medical example

2011 11th International Conference on Intelligent Systems Design and Applications, 2011
The aim of this paper is to approach causal questions in a medical domain. Causal questions par excellence are what, how and why-questions. The ‘pyramid of questions’ shows this. At the top, why-questions are the prototype of causal questions. Usually why-questions are related to scientific explanations.
Alejandro Sobrino 0001, José Angel Olivas, Cristina Puente   +2 more
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Deep causal mining for plant-wide oscillations with multilevel Granger causality analysis

2016 American Control Conference (ACC), 2016
Plant-wide disturbance such as oscillations are common in large-scale complex controlled processes whose effects propagate to many units and may deteriorate overall control performance. It is important to capture the major causal relationship within the plant and diagnose the root cause along with complete propagation paths. This paper presents a novel
Tao Yuan, Gang Li, Zhaohui Zhang, Si-Zhao Joe Qin   +3 more
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Data Mining and Causal Modeling of Customer Behaviors

Telecommunication Systems, 2002
This paper shows how to apply data-mining and modeling methods to learn predictive models of customer behaviors from survey and behavioral data. The models predict transition rates of individual customers among states, including product adds and drops and account attrition rates. A key insight is that classification tree algorithms from data mining can
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Mining Traffic Accident Data for Hazard Causality Analysis

2019 4th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), 2019
Over 1.25 million people are killed, and 20–50 million people are seriously injured by traffic accidents every year globally, according to the World Bank. This paper aims to identify patterns in traffic accident data, collected by Cyprus Police between 2007 and 2014.
Dimitrios Tasios, Christos Tjortjis, Andreas Gregoriades   +2 more
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