We introduce the notion of measurable n-sensitivity for measure preserving systems,and study the relation between measurable n-sensitivity and the maximal pattern entropy.We prove that,if(X,ℬ,μ,T)is ergodic,then(X,ℬ,...We introduce the notion of measurable n-sensitivity for measure preserving systems,and study the relation between measurable n-sensitivity and the maximal pattern entropy.We prove that,if(X,ℬ,μ,T)is ergodic,then(X,ℬ,μ,T)is measurable n-sensitive but not measurable(n+1)-sensitive if and only if h_(μ)^(*)(T)=log n,where h_(μ)^(*)(T)is the maximal pattern entropy of T.展开更多
Because mining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model ...Because mining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model only finds out the maximal frequent patterns, which can generate all frequent patterns. FP-growth algorithm is one of the most efficient frequent-pattern mining methods published so far. However, because FP-tree and conditional FP-trees must be two-way traversable, a great deal memory is needed in process of mining. This paper proposes an efficient algorithm Unid_FP-Max for mining maximal frequent patterns based on unidirectional FP-tree. Because of generation method of unidirectional FP-tree and conditional unidirectional FP-trees, the algorithm reduces the space consumption to the fullest extent. With the development of two techniques: single path pruning and header table pruning which can cut down many conditional unidirectional FP-trees generated recursively in mining process, Unid_FP-Max further lowers the expense of time and space.展开更多
Based on rough similarity degree of rough sets and close degree of fuzzy sets, the definitions of rough similarity degree and rough close degree of rough fuzzy sets are given, which can be used to measure the similar ...Based on rough similarity degree of rough sets and close degree of fuzzy sets, the definitions of rough similarity degree and rough close degree of rough fuzzy sets are given, which can be used to measure the similar degree between two rough fuzzy sets. The properties and theorems are listed. Using the two new measures, the method of clustering in the rough fuzzy system can be obtained. After clustering, the new fuzzy sample can be recognized by the principle of maximal similarity degree.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant Nos.11871188,12031019).
文摘We introduce the notion of measurable n-sensitivity for measure preserving systems,and study the relation between measurable n-sensitivity and the maximal pattern entropy.We prove that,if(X,ℬ,μ,T)is ergodic,then(X,ℬ,μ,T)is measurable n-sensitive but not measurable(n+1)-sensitive if and only if h_(μ)^(*)(T)=log n,where h_(μ)^(*)(T)is the maximal pattern entropy of T.
基金Supported by the National Natural Science Foundation of China ( No.60474022)Henan Innovation Project for University Prominent Research Talents (No.2007KYCX018)
文摘Because mining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model only finds out the maximal frequent patterns, which can generate all frequent patterns. FP-growth algorithm is one of the most efficient frequent-pattern mining methods published so far. However, because FP-tree and conditional FP-trees must be two-way traversable, a great deal memory is needed in process of mining. This paper proposes an efficient algorithm Unid_FP-Max for mining maximal frequent patterns based on unidirectional FP-tree. Because of generation method of unidirectional FP-tree and conditional unidirectional FP-trees, the algorithm reduces the space consumption to the fullest extent. With the development of two techniques: single path pruning and header table pruning which can cut down many conditional unidirectional FP-trees generated recursively in mining process, Unid_FP-Max further lowers the expense of time and space.
基金the Fujian Provincial Natural Science Foundation of China (Z0510492006J0391)
文摘Based on rough similarity degree of rough sets and close degree of fuzzy sets, the definitions of rough similarity degree and rough close degree of rough fuzzy sets are given, which can be used to measure the similar degree between two rough fuzzy sets. The properties and theorems are listed. Using the two new measures, the method of clustering in the rough fuzzy system can be obtained. After clustering, the new fuzzy sample can be recognized by the principle of maximal similarity degree.
