At present, most of the association rules algorithms are based on the Boolean attribute and single-level association rules mining. But data of the real world has various types, the multi-level and quantitative attribu...At present, most of the association rules algorithms are based on the Boolean attribute and single-level association rules mining. But data of the real world has various types, the multi-level and quantitative attributes are got more and more attention. And the most important step is to mine frequent sets. In this paper, we propose an algorithm that is called fuzzy multiple-level association （FMA） rules to mine frequent sets. It is based on the improved Eclat algorithm that is different to many researchers’ proposed algorithms thatused the Apriori algorithm. We analyze quantitative data’s frequent sets by using the fuzzy theory, dividing the hierarchy of concept and softening the boundary of attributes’ values and frequency. In this paper, we use the vertical-style data and the improved Eclat algorithm to describe the proposed method, we use this algorithm to analyze the data of Beijing logistics route. Experiments show that the algorithm has a good performance, it has better effectiveness and high efficiency.展开更多
To ensure the safety of urban rail transit operations and uncover the transmission dynam ics of risk sources,a key risk chain mining method for urban rail transit operation is pro posed.Firstly,the H-Apriori associati...To ensure the safety of urban rail transit operations and uncover the transmission dynam ics of risk sources,a key risk chain mining method for urban rail transit operation is pro posed.Firstly,the H-Apriori association rule algorithm is proposed for the characteristics of low frequency but high riskiness of high hazard degree risk sources in urban rail transit operation,which adds a new hazard degree evaluation index to the traditional Apriori algo rithm and couples with support degree two-dimensionally to mine the strong association rules among risk sources.Secondly,we construct a weighted risk network with risk sources as network nodes and strong association rules as network edges,and propose a key risk chain mining method for urban rail transit operation based on path search theory to mine key risk chains from the weighted risk network.Finally,using the actual urban rail transit operation data of a city in China as an example,a total of 17 key risk chains are mined,and then 5 key risk sources and 8 key chain break locations are obtained by riskiness and fre quency analysis of key risk chains,and control plans are proposed.The research outcomes introduce a novel approach to mining risk chains in urban rail transit operations,shedding light on the propagation mechanisms,triggering probabilities,and degrees of unsafety associated with risk sources.The results not only provide theoretical support but also offer methodological guidance for pinpointing locations of risk chain breaks and refining the control of risk sources.展开更多
基金supported by the Fundamental Research Funds for the Central Universities under Grants No.ZYGX2014J051 and No.ZYGX2014J066Science and Technology Projects in Sichuan Province under Grants No.2015JY0178,No.2016FZ0002,No.2014GZ0109,No.2015KZ002 and No.2015JY0030China Postdoctoral Science Foundation under Grant No.2015M572464
文摘At present, most of the association rules algorithms are based on the Boolean attribute and single-level association rules mining. But data of the real world has various types, the multi-level and quantitative attributes are got more and more attention. And the most important step is to mine frequent sets. In this paper, we propose an algorithm that is called fuzzy multiple-level association （FMA） rules to mine frequent sets. It is based on the improved Eclat algorithm that is different to many researchers’ proposed algorithms thatused the Apriori algorithm. We analyze quantitative data’s frequent sets by using the fuzzy theory, dividing the hierarchy of concept and softening the boundary of attributes’ values and frequency. In this paper, we use the vertical-style data and the improved Eclat algorithm to describe the proposed method, we use this algorithm to analyze the data of Beijing logistics route. Experiments show that the algorithm has a good performance, it has better effectiveness and high efficiency.
文摘To ensure the safety of urban rail transit operations and uncover the transmission dynam ics of risk sources,a key risk chain mining method for urban rail transit operation is pro posed.Firstly,the H-Apriori association rule algorithm is proposed for the characteristics of low frequency but high riskiness of high hazard degree risk sources in urban rail transit operation,which adds a new hazard degree evaluation index to the traditional Apriori algo rithm and couples with support degree two-dimensionally to mine the strong association rules among risk sources.Secondly,we construct a weighted risk network with risk sources as network nodes and strong association rules as network edges,and propose a key risk chain mining method for urban rail transit operation based on path search theory to mine key risk chains from the weighted risk network.Finally,using the actual urban rail transit operation data of a city in China as an example,a total of 17 key risk chains are mined,and then 5 key risk sources and 8 key chain break locations are obtained by riskiness and fre quency analysis of key risk chains,and control plans are proposed.The research outcomes introduce a novel approach to mining risk chains in urban rail transit operations,shedding light on the propagation mechanisms,triggering probabilities,and degrees of unsafety associated with risk sources.The results not only provide theoretical support but also offer methodological guidance for pinpointing locations of risk chain breaks and refining the control of risk sources.
