In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and impl...In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.展开更多
One of the obstacles of the efficient association rule mining is theexplosive expansion of data sets since it is costly or impossible to scan large databases, esp., formultiple times. A popular solution to improve the...One of the obstacles of the efficient association rule mining is theexplosive expansion of data sets since it is costly or impossible to scan large databases, esp., formultiple times. A popular solution to improve the speed and scalability of the association rulemining is to do the algorithm on a random sample instead of the entire database. But how toeffectively define and efficiently estimate the degree of error with respect to the outcome of thealgorithm, and how to determine the sample size needed are entangling researches until now. In thispaper, an effective and efficient algorithm is given based on the PAC (Probably Approximate Correct)learning theory to measure and estimate sample error. Then, a new adaptive, on-line, fast samplingstrategy - multi-scaling sampling - is presented inspired by MRA (Multi-Resolution Analysis) andShannon sampling theorem, for quickly obtaining acceptably approximate association rules atappropriate sample size. Both theoretical analysis and empirical study have showed that the Samplingstrategy can achieve a very good speed-accuracy trade-off.展开更多
Association rule mining plays an important role in knowledge and information discovery. Often for a dataset, a huge number of rules can be extracted, but many of them are redundant, especially in the case of multi-lev...Association rule mining plays an important role in knowledge and information discovery. Often for a dataset, a huge number of rules can be extracted, but many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant rules is a promising approach to solve this problem. However, existing work （Pasquier et al. 2005, Xu ＆ Li 2007） is only focused on single level datasets. In this paper, we firstly present a definition for redundancy and a concise representation called Reliable basis for representing non-redundant association rules, then we propose an extension to the previous work that can remove hierarchically redundant rules from multi-level datasets. We also show that the resulting concise representation of non-redundant association rules is lossless since all association rules can be derived from the representation. Experiments show that our extension can effectively generate multilevel non-redundant rules.展开更多
Complex repairable system is composed of thousands of components.Some maintenance management and decision problems in maintenance management and decision need to classify a set of components into several classes based...Complex repairable system is composed of thousands of components.Some maintenance management and decision problems in maintenance management and decision need to classify a set of components into several classes based on data mining.Furthermore,with the complexity of industrial equipment increasing,the managers should pay more attention to the key components and carry out the lean management is very important.Therefore,the idea"customer segmentation"of"precise marketing"can be used in the maintenance management of the multi-component system.Following the idea of segmentation,the components of multicomponent systems should be subdivied into groups based on specific attributes relevant to maintenance,such as maintenance cost,mean time between failures,and failure frequency.For the target specific groups of parts,the optimal maintenance policy,health assessment and maintenance scheduling can be determined.The proposed analysis framework will be given out.In order to illustrate the effectiveness of this method,a numerical example is given out.展开更多
文摘In order to discover the main causes of elevator group accidents in edge computing environment, a multi-dimensional data model of elevator accident data is established by using data cube technology, proposing and implementing a method by combining classical Apriori algorithm with the model, digging out frequent items of elevator accident data to explore the main reasons for the occurrence of elevator accidents. In addition, a collaborative edge model of elevator accidents is set to achieve data sharing, making it possible to check the detail of each cause to confirm the causes of elevator accidents. Lastly the association rules are applied to find the law of elevator Accidents.
基金CAS Project of Brain and Mind Science，国家高技术研究发展计划(863计划)，国家重点基础研究发展计划(973计划)，国家自然科学基金，湖南省自然科学基金
文摘One of the obstacles of the efficient association rule mining is theexplosive expansion of data sets since it is costly or impossible to scan large databases, esp., formultiple times. A popular solution to improve the speed and scalability of the association rulemining is to do the algorithm on a random sample instead of the entire database. But how toeffectively define and efficiently estimate the degree of error with respect to the outcome of thealgorithm, and how to determine the sample size needed are entangling researches until now. In thispaper, an effective and efficient algorithm is given based on the PAC (Probably Approximate Correct)learning theory to measure and estimate sample error. Then, a new adaptive, on-line, fast samplingstrategy - multi-scaling sampling - is presented inspired by MRA (Multi-Resolution Analysis) andShannon sampling theorem, for quickly obtaining acceptably approximate association rules atappropriate sample size. Both theoretical analysis and empirical study have showed that the Samplingstrategy can achieve a very good speed-accuracy trade-off.
文摘Association rule mining plays an important role in knowledge and information discovery. Often for a dataset, a huge number of rules can be extracted, but many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant rules is a promising approach to solve this problem. However, existing work （Pasquier et al. 2005, Xu ＆ Li 2007） is only focused on single level datasets. In this paper, we firstly present a definition for redundancy and a concise representation called Reliable basis for representing non-redundant association rules, then we propose an extension to the previous work that can remove hierarchically redundant rules from multi-level datasets. We also show that the resulting concise representation of non-redundant association rules is lossless since all association rules can be derived from the representation. Experiments show that our extension can effectively generate multilevel non-redundant rules.
基金National Natural Science Foundations of China(No.71501103)Natural Science Foundation of Inner Mongolia,China(No.2015BS0705)the Program of Higher-Level Talents of Inner Mongolia University,China(No.20700-5145131)
文摘Complex repairable system is composed of thousands of components.Some maintenance management and decision problems in maintenance management and decision need to classify a set of components into several classes based on data mining.Furthermore,with the complexity of industrial equipment increasing,the managers should pay more attention to the key components and carry out the lean management is very important.Therefore,the idea"customer segmentation"of"precise marketing"can be used in the maintenance management of the multi-component system.Following the idea of segmentation,the components of multicomponent systems should be subdivied into groups based on specific attributes relevant to maintenance,such as maintenance cost,mean time between failures,and failure frequency.For the target specific groups of parts,the optimal maintenance policy,health assessment and maintenance scheduling can be determined.The proposed analysis framework will be given out.In order to illustrate the effectiveness of this method,a numerical example is given out.
