Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence w...Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence were redefined. The algorithm can mine the association rules with decision attributes directly without processing missing values. Using the incomplete dataset Mushroom from UCI machine learning repository, the new algorithm was compared with the classical association rules mining algorithm based on Apriori from the number of rules extracted, testing accuracy and execution time. The experiment results show that the new algorithm has advantages of short execution time and high accuracy.展开更多
A new algorithm for mining quantitative association rules with standard SQL is presented. The association rules are evaluated with the sufficiency gene LS of subjectivity Bayes reasoning. This algorithm is proved to b...A new algorithm for mining quantitative association rules with standard SQL is presented. The association rules are evaluated with the sufficiency gene LS of subjectivity Bayes reasoning. This algorithm is proved to be quick and effective with its application in Lujiang insects and pests database.展开更多
Aiming at the research that using more new knowledge to develope knowledge system with dynamic accordance, and under the background of using Fuzzy language field and Fuzzy language values structure as description fram...Aiming at the research that using more new knowledge to develope knowledge system with dynamic accordance, and under the background of using Fuzzy language field and Fuzzy language values structure as description framework, the generalized cell Automation that can synthetically process fuzzy indeterminacy and random indeterminacy and generalized inductive logic causal model is brought forward. On this basis, a kind of the new method that can discover causal association rules is provded. According to the causal information of standard sample space and commonly sample space, through constructing its state （abnormality） relation matrix, causal association rules can be gained by using inductive reasoning mechanism. The estimate of this algorithm complexity is given,and its validiw is proved through case.展开更多
The conventional complete association rule set was replaced by the least association rule set in data warehouse association rule mining process. The least association rule set should comply with two requirements: 1) i...The conventional complete association rule set was replaced by the least association rule set in data warehouse association rule mining process. The least association rule set should comply with two requirements: 1) it should be the minimal and the simplest association rule set; 2) its predictive power should in no way be weaker than that of the complete association rule set so that the precision of the association rule set analysis can be guaranteed. By adopting the least association rule set, the pruning of weak rules can be effectively carried out so as to greatly reduce the number of frequent itemset, and therefore improve the mining efficiency. Finally, based on the classical Apriori algorithm, the upward closure property of weak rules is utilized to develop a corresponding efficient algorithm.展开更多
OBJECTIVE To identify compound combinations as candidate multi-component drugs for the type 2 diabetes from natural product information.METHODS Chemical composition information of herbs in natural medicine was acquire...OBJECTIVE To identify compound combinations as candidate multi-component drugs for the type 2 diabetes from natural product information.METHODS Chemical composition information of herbs in natural medicine was acquired by integrating conventional databases;Traditional Chinese Medicine Information Database(TCM-ID)and Traditional Chinese Medicine Integrated Database(TCMID).Therapeutic effect of each herb on the type 2 diabetes was examined by analyzing annotated function information with a text-mining method.The Apriori algorithm,which is a classical method for extracting associations between object in large-scale databases,was employed to infer association rules between compound combinations and therapeutic effect on the target disease.The chemical composition and therapeutic information of each herb was used as a transaction,which consists of the chemical compound combination as an antecedent item set and the therapeutic effect as a consequent item.The association rules with high support and confidence value were suggested as candidate multi-component drugs for the type 2 diabetes.RESULTS Totally 40 941 association rules were inferred with support lower bound 0.05% and maximum rule length 4.With respect to support and confidence,the top-ranked compound combination was puerarin and daidzin(support=0.15%,confidence=100%).In addition,the top 16 compound combinations were composed of 11 individual chemical compounds;puerarin,daidzin,abscisic acid,batatisine,dopamine,cholesterol,daidzein,gamma-aminobutyric acid,stigmasterol,campesteryl ferulate,and campesterol.To validate therapeutic effect of the proposed compound combinations,literature evidences of each individual compound were investigated.Among the 11 individual compounds,six compounds were reported to be effective for the treatment of the diabetes mellitus.CONCLUSION By analyzing natural product in formation with association rule mining,16 compound combinations are suggested as candidate multi-component drugs for the type 2 diabetes.These compound combinations are recommended for further investigation in the context of drug development.展开更多
Analyzing systemically more than 550 Li Dong Y uan’s formula of spleen and stomach by using the association rule to mine the in formation relativity between formula, herbal medicine and syndrome.From this we can stud...Analyzing systemically more than 550 Li Dong Y uan’s formula of spleen and stomach by using the association rule to mine the in formation relativity between formula, herbal medicine and syndrome.From this we can study better the compatibility regulations of the展开更多
With the popularization of social media,public opi-nion information on emergencies spreads rapidly on the Internet,the impact of negative public opinions on an event has become more significant.Based on the organizati...With the popularization of social media,public opi-nion information on emergencies spreads rapidly on the Internet,the impact of negative public opinions on an event has become more significant.Based on the organizational form of public opinion information,the knowledge graph is used to construct the knowledge base of public opinion risk cases on the emer-gency network.The emotion recognition model of negative pub-lic opinion information based on the bi-directional long short-term memory(BiLSTM)network is studied in the model layer design,and a linear discriminant analysis(LDA)topic extraction method combined with association rules is proposed to extract and mine the semantics of negative public opinion topics to real-ize further in-depth analysis of information topics.Focusing on public health emergencies,knowledge acquisition and knowl-edge processing of public opinion information are conducted,and the experimental results show that the knowledge graph framework based on the construction can facilitate in-depth theme evolution analysis of public opinion events,thus demon-strating important research significance for reducing online pub-lic opinion risks.展开更多
基金Projects(10871031, 60474070) supported by the National Natural Science Foundation of ChinaProject(07A001) supported by the Scientific Research Fund of Hunan Provincial Education Department, China
文摘Based on the rough set theory which is a powerful tool in dealing with vagueness and uncertainty, an algorithm to mine association rules in incomplete information systems was presented and the support and confidence were redefined. The algorithm can mine the association rules with decision attributes directly without processing missing values. Using the incomplete dataset Mushroom from UCI machine learning repository, the new algorithm was compared with the classical association rules mining algorithm based on Apriori from the number of rules extracted, testing accuracy and execution time. The experiment results show that the new algorithm has advantages of short execution time and high accuracy.
文摘A new algorithm for mining quantitative association rules with standard SQL is presented. The association rules are evaluated with the sufficiency gene LS of subjectivity Bayes reasoning. This algorithm is proved to be quick and effective with its application in Lujiang insects and pests database.
文摘Aiming at the research that using more new knowledge to develope knowledge system with dynamic accordance, and under the background of using Fuzzy language field and Fuzzy language values structure as description framework, the generalized cell Automation that can synthetically process fuzzy indeterminacy and random indeterminacy and generalized inductive logic causal model is brought forward. On this basis, a kind of the new method that can discover causal association rules is provded. According to the causal information of standard sample space and commonly sample space, through constructing its state （abnormality） relation matrix, causal association rules can be gained by using inductive reasoning mechanism. The estimate of this algorithm complexity is given,and its validiw is proved through case.
文摘The conventional complete association rule set was replaced by the least association rule set in data warehouse association rule mining process. The least association rule set should comply with two requirements: 1) it should be the minimal and the simplest association rule set; 2) its predictive power should in no way be weaker than that of the complete association rule set so that the precision of the association rule set analysis can be guaranteed. By adopting the least association rule set, the pruning of weak rules can be effectively carried out so as to greatly reduce the number of frequent itemset, and therefore improve the mining efficiency. Finally, based on the classical Apriori algorithm, the upward closure property of weak rules is utilized to develop a corresponding efficient algorithm.
基金The project supported by the Bio-Synergy Research Project(NRF-2012M3A9C4048758)of the Ministry of Science,ICT and Future Planning through the National Research Foundation
文摘OBJECTIVE To identify compound combinations as candidate multi-component drugs for the type 2 diabetes from natural product information.METHODS Chemical composition information of herbs in natural medicine was acquired by integrating conventional databases;Traditional Chinese Medicine Information Database(TCM-ID)and Traditional Chinese Medicine Integrated Database(TCMID).Therapeutic effect of each herb on the type 2 diabetes was examined by analyzing annotated function information with a text-mining method.The Apriori algorithm,which is a classical method for extracting associations between object in large-scale databases,was employed to infer association rules between compound combinations and therapeutic effect on the target disease.The chemical composition and therapeutic information of each herb was used as a transaction,which consists of the chemical compound combination as an antecedent item set and the therapeutic effect as a consequent item.The association rules with high support and confidence value were suggested as candidate multi-component drugs for the type 2 diabetes.RESULTS Totally 40 941 association rules were inferred with support lower bound 0.05% and maximum rule length 4.With respect to support and confidence,the top-ranked compound combination was puerarin and daidzin(support=0.15%,confidence=100%).In addition,the top 16 compound combinations were composed of 11 individual chemical compounds;puerarin,daidzin,abscisic acid,batatisine,dopamine,cholesterol,daidzein,gamma-aminobutyric acid,stigmasterol,campesteryl ferulate,and campesterol.To validate therapeutic effect of the proposed compound combinations,literature evidences of each individual compound were investigated.Among the 11 individual compounds,six compounds were reported to be effective for the treatment of the diabetes mellitus.CONCLUSION By analyzing natural product in formation with association rule mining,16 compound combinations are suggested as candidate multi-component drugs for the type 2 diabetes.These compound combinations are recommended for further investigation in the context of drug development.
文摘Analyzing systemically more than 550 Li Dong Y uan’s formula of spleen and stomach by using the association rule to mine the in formation relativity between formula, herbal medicine and syndrome.From this we can study better the compatibility regulations of the
基金supported by the National Social Science Foundation Major Project(22&ZD135)the National Social Science Fund National Emergency Management System Construction Research Project(20VYJ061).
文摘With the popularization of social media,public opi-nion information on emergencies spreads rapidly on the Internet,the impact of negative public opinions on an event has become more significant.Based on the organizational form of public opinion information,the knowledge graph is used to construct the knowledge base of public opinion risk cases on the emer-gency network.The emotion recognition model of negative pub-lic opinion information based on the bi-directional long short-term memory(BiLSTM)network is studied in the model layer design,and a linear discriminant analysis(LDA)topic extraction method combined with association rules is proposed to extract and mine the semantics of negative public opinion topics to real-ize further in-depth analysis of information topics.Focusing on public health emergencies,knowledge acquisition and knowl-edge processing of public opinion information are conducted,and the experimental results show that the knowledge graph framework based on the construction can facilitate in-depth theme evolution analysis of public opinion events,thus demon-strating important research significance for reducing online pub-lic opinion risks.
