With the gradual acceleration of information construction in colleges and universities,digital campus and smart campus have gradually become important means for colleges and universities to scientifically manage the c...With the gradual acceleration of information construction in colleges and universities,digital campus and smart campus have gradually become important means for colleges and universities to scientifically manage the campus.They have been applied to teaching,scientific research,student management,and other fields,improving the quality and efficiency of management.This paper mainly studies the intelligent educational administration management system based on data mining technology.Firstly,this paper introduces the application process of data mining technology,and builds an intelligent educational administration management system based on data mining technology.Then,this paper optimizes the application of the Apriori algorithm in educational administration management through transaction compression and frequent sampling.Compared with the traditional Apriori algorithm,the optimized Apriori algorithm in this paper has a shorter execution time under the same minimum support.展开更多
Objective:To analyze the component law of Chinese patent medicines for anti-influenza and develop new prescriptions for anti-influenza by unsupervised data mining methods. Methods: Chinese patent medicine recipes for ...Objective:To analyze the component law of Chinese patent medicines for anti-influenza and develop new prescriptions for anti-influenza by unsupervised data mining methods. Methods: Chinese patent medicine recipes for anti-influenza were collected and recorded in the database, and then the correlation coefficient between herbs, core combinations of herbs and new prescriptions were analyzed by using modified mutual information, complex system entropy cluster and unsupervised hierarchical clustering, respectively. Results: Based on analysis of 126 Chinese patent medicine recipes, the frequency of each herb occurrence in these recipes, 54 frequently-used herb pairs, 34 core combinations were determined, and 4 new recipes for influenza were developed. Conclusion: Unsupervised data mining methods are able to mine the component law quickly and develop new prescriptions.展开更多
Land resources are facing crises of being misused,especially for an intersection area between town and country,and land control has to be enforced.This paper presents a development of data mining method for land contr...Land resources are facing crises of being misused,especially for an intersection area between town and country,and land control has to be enforced.This paper presents a development of data mining method for land control.A vector_match method for the prerequisite of data mining i.e., data cleaning is proposed,which deals with both character and numeric data via vectorizing character_string and matching number.A minimal decision algorithm of rough set is used to discover the knowledge hidden in the data warehouse.In order to monitor land use dynamically and accurately,it is suggested to set up a real_time land control system based on GPS,digital photogrammetry and online data mining.Finally,the means is applied in the intersection area between town and country of Wuhan city,and a set of knowledge about land control is discovered.展开更多
An experience is presented using the finite element method （FEM） and data mining （DM） techniques to develop models that can be used to optimieze the skin-pass rolling process based on its operating conditions. A F...An experience is presented using the finite element method （FEM） and data mining （DM） techniques to develop models that can be used to optimieze the skin-pass rolling process based on its operating conditions. A FE model based on a real skin-pass process is built and validated. Based on this model, a group of FE models is simulated with different adjustment parameters and with different materials for the sheet; both variables are chosen from pre-set ranges, From all FE model simulations, a database is generated; this database is made up of the above mentioned adjustment parameters, sheet properties and the variables of the process arising from the simulation of the model. Various types of data mining algorithms are used to develop predictive models for each of the variables of the process.The best predictive models can be used to predict experimentally hard-to-measure variables （internal stresses, internal straine, etc.） which are useful in the optimal design of the process or to be applied in real time control systems of a skin-pass process in -plant.展开更多
This paper integrates genetic algorithm and neura l network techniques to build new temporal predicting analysis tools for geographic information system (GIS). These new GIS tools can be readily applied in a practical...This paper integrates genetic algorithm and neura l network techniques to build new temporal predicting analysis tools for geographic information system (GIS). These new GIS tools can be readily applied in a practical and appropriate manner in spatial and temp oral research to patch the gaps in GIS data mining and knowledge discovery functions. The specific achievement here is the integration of related artificial intellig ent technologies into GIS software to establish a conceptual spatial and temporal analysis framework. And, by using this framework to develop an artificial intelligent spatial and tempor al information analyst (ASIA) system which then is fully utilized in the existin g GIS package. This study of air pollutants forecasting provides a geographical practical case to prove the rationalization and justness of the conceptual tempo ral analysis framework.展开更多
Edit distance measures the similarity between two strings (as the minimum number of change, insert or delete operations that transform one string to the other). An edit sequence s is a sequence of such operations and ...Edit distance measures the similarity between two strings (as the minimum number of change, insert or delete operations that transform one string to the other). An edit sequence s is a sequence of such operations and can be used to represent the string resulting from applying s to a reference string. We present a modification to Ukkonen’s edit distance calculating algorithm based upon representing strings by edit sequences. We conclude with a demonstration of how using this representation can improve mitochondrial DNA query throughput performance in a distributed computing environment.展开更多
In this research article, we analyze the multimedia data mining and classification algorithm based on database optimization techniques. Of high performance application requirements of various kinds are springing up co...In this research article, we analyze the multimedia data mining and classification algorithm based on database optimization techniques. Of high performance application requirements of various kinds are springing up constantly makes parallel computer system structure is valued by more and more common but the corresponding software system development lags far behind the development of the hardware system, it is more obvious in the field of database technology application. Multimedia mining is different from the low level of computer multimedia processing technology and the former focuses on the extracted from huge multimedia collection mode which focused on specific features of understanding or extraction from a single multimedia objects. Our research provides new paradigm for the methodology which will be meaningful and necessary.展开更多
The study aims to recognize how efficiently Educational DataMining(EDM)integrates into Artificial Intelligence(AI)to develop skills for predicting students’performance.The study used a survey questionnaire and collec...The study aims to recognize how efficiently Educational DataMining(EDM)integrates into Artificial Intelligence(AI)to develop skills for predicting students’performance.The study used a survey questionnaire and collected data from 300 undergraduate students of Al Neelain University.The first step’s initial population placements were created using Particle Swarm Optimization(PSO).Then,using adaptive feature space search,Educational Grey Wolf Optimization(EGWO)was employed to choose the optimal attribute combination.The second stage uses the SVMclassifier to forecast classification accuracy.Different classifiers were utilized to evaluate the performance of students.According to the results,it was revealed that AI could forecast the final grades of students with an accuracy rate of 97%on the test dataset.Furthermore,the present study showed that successful students could be selected by the Decision Tree model with an efficiency rate of 87.50%and could be categorized as having equal information ratio gain after the semester.While the random forest provided an accuracy of 28%.These findings indicate the higher accuracy rate in the results when these models were implemented on the data set which provides significantly accurate results as compared to a linear regression model with accuracy(12%).The study concluded that the methodology used in this study can prove to be helpful for students and teachers in upgrading academic performance,reducing chances of failure,and taking appropriate steps at the right time to raise the standards of education.The study also motivates academics to assess and discover EDM at several other universities.展开更多
This paper considers the problem of applying data mining techniques to aeronautical field.The truncation method,which is one of the techniques in the aeronautical data mining,can be used to efficiently handle the air-...This paper considers the problem of applying data mining techniques to aeronautical field.The truncation method,which is one of the techniques in the aeronautical data mining,can be used to efficiently handle the air-combat behavior data.The technique of air-combat behavior data mining based on the truncation method is proposed to discover the air-combat rules or patterns.The simulation platform of the air-combat behavior data mining that supports two fighters is implemented.The simulation experimental results show that the proposed air-combat behavior data mining technique based on the truncation method is feasible whether in efficiency or in effectiveness.展开更多
Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of informatio...Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that help</span><span style="font-family:Verdana;"><span style="font-family:Verdana;">s</span></span><span style="font-family:Verdana;"> to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services </span><span style="font-family:Verdana;"><span style="font-family:Verdana;">is</span></span><span style="font-family:Verdana;"> depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Na<span style="white-space:nowrap;">ï</span>ve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students’ records to predict degree completion time.展开更多
A new method was worked out to improve the precision of springback prediction in sheet metal forming by combining the finite element method (FEM) with the data mining (DM) technique. First the genetic algorithm (GA) w...A new method was worked out to improve the precision of springback prediction in sheet metal forming by combining the finite element method (FEM) with the data mining (DM) technique. First the genetic algorithm (GA) was adopted for recognizing the material parameters. Then according to the even design idea, the suitable calculation scheme was confirmed, and FEM was used for calculating the springback. The computation results were compared with experiment data, the difference between them was taken as source data, and a new pattern recognition method of DM called hierarchical optimal map recognition method (HOMR) is applied for summarizing the calculation regulation in FEM. At the end, the mathematics model of the springback simulation was established. Based on the model, the calculation errors of springback can be controlled within 10% compared with the experimental results.展开更多
Travelling Salesman Problem(TSP) is a classical optimization problem and it is one of a class of NP-Problem.The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by ...Travelling Salesman Problem(TSP) is a classical optimization problem and it is one of a class of NP-Problem.The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an Ant Colony Algorithm(ACA) performing a searching operation and to develop a rule set searcher which approximates the ACA′s searcher.An attribute-oriented induction methodology was used to explore the relationship between an operations′ sequence and its attributes and a set of rules has been developed.At the end of this paper,the experimental results have shown that the proposed approach has good performance with respect to the quality of solution and the speed of computation.展开更多
An intrusion detection （ID） model is proposed based on the fuzzy data mining method. A major difficulty of anomaly ID is that patterns of the normal behavior change with time. In addition, an actual intrusion with a...An intrusion detection （ID） model is proposed based on the fuzzy data mining method. A major difficulty of anomaly ID is that patterns of the normal behavior change with time. In addition, an actual intrusion with a small deviation may match normal patterns. So the intrusion behavior cannot be detected by the detection system.To solve the problem, fuzzy data mining technique is utilized to extract patterns representing the normal behavior of a network. A set of fuzzy association rules mined from the network data are shown as a model of “normal behaviors”. To detect anomalous behaviors, fuzzy association rules are generated from new audit data and the similarity with sets mined from “normal” data is computed. If the similarity values are lower than a threshold value,an alarm is given. Furthermore, genetic algorithms are used to adjust the fuzzy membership functions and to select an appropriate set of features.展开更多
Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.Th...Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.展开更多
To make business policy, market analysis, corporate decision, fraud detection, etc., we have to analyze and work with huge amount of data. Generally, such data are taken from different sources. Researchers are using d...To make business policy, market analysis, corporate decision, fraud detection, etc., we have to analyze and work with huge amount of data. Generally, such data are taken from different sources. Researchers are using data mining to perform such tasks. Data mining techniques are used to find hidden information from large data source. Data mining is using for various fields: Artificial intelligence, Bank, health and medical, corruption, legal issues, corporate business, marketing, etc. Special interest is given to associate rules, data mining algorithms, decision tree and distributed approach. Data is becoming larger and spreading geographically. So it is difficult to find better result from only a central data source. For knowledge discovery, we have to work with distributed database. On the other hand, security and privacy considerations are also another factor for de-motivation of working with centralized data. For this reason, distributed database is essential for future processing. In this paper, we have proposed a framework to study data mining in distributed environment. The paper presents a framework to bring out actionable knowledge. We have shown some level by which we can generate actionable knowledge. Possible tools and technique for these levels are discussed.展开更多
With the gradual development of data mining technology,more and more data mining software emerges as the times require,and the data mining methods are diversified,which provides a strong support in the study of the tr...With the gradual development of data mining technology,more and more data mining software emerges as the times require,and the data mining methods are diversified,which provides a strong support in the study of the treatment of hypertension by traditional Chinese medicine.This research systematically introduces the advantages,disadvantages and application examples of data mining software such as SPSS,TCM inheritance support platform,TCM clinical research information sharing system and data mining methods such as cluster analysis,bayesian network,system evaluation.It is expected to enhance the practical application of data mining in the study of prevention,diagnosis and medication rule of TCM treatment of hypertension,and provide reference for the development of new software and new technology.展开更多
Objective: According to RFM model theory of customer relationship management, data mining technology was used to group the chronic infectious disease patients to explore the effect of customer segmentation on the mana...Objective: According to RFM model theory of customer relationship management, data mining technology was used to group the chronic infectious disease patients to explore the effect of customer segmentation on the management of patients with different characteristics. Methods: 170,246 outpatient data was extracted from the hospital management information system (HIS) during January 2016 to July 2016, 43,448 data was formed after the data cleaning. K-Means clustering algorithm was used to classify patients with chronic infectious diseases, and then C5.0 decision tree algorithm was used to predict the situation of patients with chronic infectious diseases. Results: Male patients accounted for 58.7%, patients living in Shanghai accounted for 85.6%. The average age of patients is 45.88 years old, the high incidence age is 25 to 65 years old. Patients was gathered into three categories: 1) Clusters 1—Important patients (4786 people, 11.72%, R = 2.89, F = 11.72, M = 84,302.95);2) Clustering 2—Major patients (23,103, 53.2%, R = 5.22, F = 3.45, M = 9146.39);3) Cluster 3—Potential patients (15,559 people, 35.8%, R = 19.77, F = 1.55, M = 1739.09). C5.0 decision tree algorithm was used to predict the treatment situation of patients with chronic infectious diseases, the final treatment time (weeks) is an important predictor, the accuracy rate is 99.94% verified by the confusion model. Conclusion: Medical institutions should strengthen the adherence education for patients with chronic infectious diseases, establish the chronic infectious diseases and customer relationship management database, take the initiative to help them improve treatment adherence. Chinese governments at all levels should speed up the construction of hospital information, establish the chronic infectious disease database, strengthen the blocking of mother-to-child transmission, to effectively curb chronic infectious diseases, reduce disease burden and mortality.展开更多
文摘With the gradual acceleration of information construction in colleges and universities,digital campus and smart campus have gradually become important means for colleges and universities to scientifically manage the campus.They have been applied to teaching,scientific research,student management,and other fields,improving the quality and efficiency of management.This paper mainly studies the intelligent educational administration management system based on data mining technology.Firstly,this paper introduces the application process of data mining technology,and builds an intelligent educational administration management system based on data mining technology.Then,this paper optimizes the application of the Apriori algorithm in educational administration management through transaction compression and frequent sampling.Compared with the traditional Apriori algorithm,the optimized Apriori algorithm in this paper has a shorter execution time under the same minimum support.
基金supported by Scientific Research Special Project of TCM Profession (200907001E)Science and Technology Special Major Project for "Significant New Drugs Formulation" (2009ZX09301-005-02)
文摘Objective:To analyze the component law of Chinese patent medicines for anti-influenza and develop new prescriptions for anti-influenza by unsupervised data mining methods. Methods: Chinese patent medicine recipes for anti-influenza were collected and recorded in the database, and then the correlation coefficient between herbs, core combinations of herbs and new prescriptions were analyzed by using modified mutual information, complex system entropy cluster and unsupervised hierarchical clustering, respectively. Results: Based on analysis of 126 Chinese patent medicine recipes, the frequency of each herb occurrence in these recipes, 54 frequently-used herb pairs, 34 core combinations were determined, and 4 new recipes for influenza were developed. Conclusion: Unsupervised data mining methods are able to mine the component law quickly and develop new prescriptions.
基金ProjectsupportedbyResearchGrantofHongkongPolytechricUniversity (No .1 .34 .37.970 9) andNationalNatureScienceFoundationofChi
文摘Land resources are facing crises of being misused,especially for an intersection area between town and country,and land control has to be enforced.This paper presents a development of data mining method for land control.A vector_match method for the prerequisite of data mining i.e., data cleaning is proposed,which deals with both character and numeric data via vectorizing character_string and matching number.A minimal decision algorithm of rough set is used to discover the knowledge hidden in the data warehouse.In order to monitor land use dynamically and accurately,it is suggested to set up a real_time land control system based on GPS,digital photogrammetry and online data mining.Finally,the means is applied in the intersection area between town and country of Wuhan city,and a set of knowledge about land control is discovered.
基金Item Sponsored by Spanish Ministry of Education and Science(DPI2007-61090)European Commission Research Programme of the Research Fund for Coal and Steel(RFS-PR-06035)
文摘An experience is presented using the finite element method （FEM） and data mining （DM） techniques to develop models that can be used to optimieze the skin-pass rolling process based on its operating conditions. A FE model based on a real skin-pass process is built and validated. Based on this model, a group of FE models is simulated with different adjustment parameters and with different materials for the sheet; both variables are chosen from pre-set ranges, From all FE model simulations, a database is generated; this database is made up of the above mentioned adjustment parameters, sheet properties and the variables of the process arising from the simulation of the model. Various types of data mining algorithms are used to develop predictive models for each of the variables of the process.The best predictive models can be used to predict experimentally hard-to-measure variables （internal stresses, internal straine, etc.） which are useful in the optimal design of the process or to be applied in real time control systems of a skin-pass process in -plant.
文摘This paper integrates genetic algorithm and neura l network techniques to build new temporal predicting analysis tools for geographic information system (GIS). These new GIS tools can be readily applied in a practical and appropriate manner in spatial and temp oral research to patch the gaps in GIS data mining and knowledge discovery functions. The specific achievement here is the integration of related artificial intellig ent technologies into GIS software to establish a conceptual spatial and temporal analysis framework. And, by using this framework to develop an artificial intelligent spatial and tempor al information analyst (ASIA) system which then is fully utilized in the existin g GIS package. This study of air pollutants forecasting provides a geographical practical case to prove the rationalization and justness of the conceptual tempo ral analysis framework.
文摘Edit distance measures the similarity between two strings (as the minimum number of change, insert or delete operations that transform one string to the other). An edit sequence s is a sequence of such operations and can be used to represent the string resulting from applying s to a reference string. We present a modification to Ukkonen’s edit distance calculating algorithm based upon representing strings by edit sequences. We conclude with a demonstration of how using this representation can improve mitochondrial DNA query throughput performance in a distributed computing environment.
文摘In this research article, we analyze the multimedia data mining and classification algorithm based on database optimization techniques. Of high performance application requirements of various kinds are springing up constantly makes parallel computer system structure is valued by more and more common but the corresponding software system development lags far behind the development of the hardware system, it is more obvious in the field of database technology application. Multimedia mining is different from the low level of computer multimedia processing technology and the former focuses on the extracted from huge multimedia collection mode which focused on specific features of understanding or extraction from a single multimedia objects. Our research provides new paradigm for the methodology which will be meaningful and necessary.
基金supported via funding from Prince Sattam bin Abdulaziz University Project Number(PSAU/2024/R/1445).
文摘The study aims to recognize how efficiently Educational DataMining(EDM)integrates into Artificial Intelligence(AI)to develop skills for predicting students’performance.The study used a survey questionnaire and collected data from 300 undergraduate students of Al Neelain University.The first step’s initial population placements were created using Particle Swarm Optimization(PSO).Then,using adaptive feature space search,Educational Grey Wolf Optimization(EGWO)was employed to choose the optimal attribute combination.The second stage uses the SVMclassifier to forecast classification accuracy.Different classifiers were utilized to evaluate the performance of students.According to the results,it was revealed that AI could forecast the final grades of students with an accuracy rate of 97%on the test dataset.Furthermore,the present study showed that successful students could be selected by the Decision Tree model with an efficiency rate of 87.50%and could be categorized as having equal information ratio gain after the semester.While the random forest provided an accuracy of 28%.These findings indicate the higher accuracy rate in the results when these models were implemented on the data set which provides significantly accurate results as compared to a linear regression model with accuracy(12%).The study concluded that the methodology used in this study can prove to be helpful for students and teachers in upgrading academic performance,reducing chances of failure,and taking appropriate steps at the right time to raise the standards of education.The study also motivates academics to assess and discover EDM at several other universities.
文摘This paper considers the problem of applying data mining techniques to aeronautical field.The truncation method,which is one of the techniques in the aeronautical data mining,can be used to efficiently handle the air-combat behavior data.The technique of air-combat behavior data mining based on the truncation method is proposed to discover the air-combat rules or patterns.The simulation platform of the air-combat behavior data mining that supports two fighters is implemented.The simulation experimental results show that the proposed air-combat behavior data mining technique based on the truncation method is feasible whether in efficiency or in effectiveness.
文摘Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that help</span><span style="font-family:Verdana;"><span style="font-family:Verdana;">s</span></span><span style="font-family:Verdana;"> to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services </span><span style="font-family:Verdana;"><span style="font-family:Verdana;">is</span></span><span style="font-family:Verdana;"> depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Na<span style="white-space:nowrap;">ï</span>ve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students’ records to predict degree completion time.
文摘A new method was worked out to improve the precision of springback prediction in sheet metal forming by combining the finite element method (FEM) with the data mining (DM) technique. First the genetic algorithm (GA) was adopted for recognizing the material parameters. Then according to the even design idea, the suitable calculation scheme was confirmed, and FEM was used for calculating the springback. The computation results were compared with experiment data, the difference between them was taken as source data, and a new pattern recognition method of DM called hierarchical optimal map recognition method (HOMR) is applied for summarizing the calculation regulation in FEM. At the end, the mathematics model of the springback simulation was established. Based on the model, the calculation errors of springback can be controlled within 10% compared with the experimental results.
文摘Travelling Salesman Problem(TSP) is a classical optimization problem and it is one of a class of NP-Problem.The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an Ant Colony Algorithm(ACA) performing a searching operation and to develop a rule set searcher which approximates the ACA′s searcher.An attribute-oriented induction methodology was used to explore the relationship between an operations′ sequence and its attributes and a set of rules has been developed.At the end of this paper,the experimental results have shown that the proposed approach has good performance with respect to the quality of solution and the speed of computation.
文摘An intrusion detection （ID） model is proposed based on the fuzzy data mining method. A major difficulty of anomaly ID is that patterns of the normal behavior change with time. In addition, an actual intrusion with a small deviation may match normal patterns. So the intrusion behavior cannot be detected by the detection system.To solve the problem, fuzzy data mining technique is utilized to extract patterns representing the normal behavior of a network. A set of fuzzy association rules mined from the network data are shown as a model of “normal behaviors”. To detect anomalous behaviors, fuzzy association rules are generated from new audit data and the similarity with sets mined from “normal” data is computed. If the similarity values are lower than a threshold value,an alarm is given. Furthermore, genetic algorithms are used to adjust the fuzzy membership functions and to select an appropriate set of features.
基金supported by the Science and Technology Project of China Southern Power Grid(GZHKJXM20210043-080041KK52210002).
文摘Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.
文摘To make business policy, market analysis, corporate decision, fraud detection, etc., we have to analyze and work with huge amount of data. Generally, such data are taken from different sources. Researchers are using data mining to perform such tasks. Data mining techniques are used to find hidden information from large data source. Data mining is using for various fields: Artificial intelligence, Bank, health and medical, corruption, legal issues, corporate business, marketing, etc. Special interest is given to associate rules, data mining algorithms, decision tree and distributed approach. Data is becoming larger and spreading geographically. So it is difficult to find better result from only a central data source. For knowledge discovery, we have to work with distributed database. On the other hand, security and privacy considerations are also another factor for de-motivation of working with centralized data. For this reason, distributed database is essential for future processing. In this paper, we have proposed a framework to study data mining in distributed environment. The paper presents a framework to bring out actionable knowledge. We have shown some level by which we can generate actionable knowledge. Possible tools and technique for these levels are discussed.
基金supported by the National Natural Science Foundation of Hebei Province(Grant No:H2015423020)the Graduate Innovation Funding Projects of Hebei Province(Grant No:XCXZZSS2020022).
文摘With the gradual development of data mining technology,more and more data mining software emerges as the times require,and the data mining methods are diversified,which provides a strong support in the study of the treatment of hypertension by traditional Chinese medicine.This research systematically introduces the advantages,disadvantages and application examples of data mining software such as SPSS,TCM inheritance support platform,TCM clinical research information sharing system and data mining methods such as cluster analysis,bayesian network,system evaluation.It is expected to enhance the practical application of data mining in the study of prevention,diagnosis and medication rule of TCM treatment of hypertension,and provide reference for the development of new software and new technology.
文摘Objective: According to RFM model theory of customer relationship management, data mining technology was used to group the chronic infectious disease patients to explore the effect of customer segmentation on the management of patients with different characteristics. Methods: 170,246 outpatient data was extracted from the hospital management information system (HIS) during January 2016 to July 2016, 43,448 data was formed after the data cleaning. K-Means clustering algorithm was used to classify patients with chronic infectious diseases, and then C5.0 decision tree algorithm was used to predict the situation of patients with chronic infectious diseases. Results: Male patients accounted for 58.7%, patients living in Shanghai accounted for 85.6%. The average age of patients is 45.88 years old, the high incidence age is 25 to 65 years old. Patients was gathered into three categories: 1) Clusters 1—Important patients (4786 people, 11.72%, R = 2.89, F = 11.72, M = 84,302.95);2) Clustering 2—Major patients (23,103, 53.2%, R = 5.22, F = 3.45, M = 9146.39);3) Cluster 3—Potential patients (15,559 people, 35.8%, R = 19.77, F = 1.55, M = 1739.09). C5.0 decision tree algorithm was used to predict the treatment situation of patients with chronic infectious diseases, the final treatment time (weeks) is an important predictor, the accuracy rate is 99.94% verified by the confusion model. Conclusion: Medical institutions should strengthen the adherence education for patients with chronic infectious diseases, establish the chronic infectious diseases and customer relationship management database, take the initiative to help them improve treatment adherence. Chinese governments at all levels should speed up the construction of hospital information, establish the chronic infectious disease database, strengthen the blocking of mother-to-child transmission, to effectively curb chronic infectious diseases, reduce disease burden and mortality.
