A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking in...A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-com-merce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy data.This work pre-sents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)algorithm.The systolic tree is used for pattern mining,and based on this,the recommendations are given.The proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved.展开更多
Traditional machine-learning algorithms are struggling to handle the exceedingly large amount of data being generated by the internet. In real-world applications, there is an urgent need for machine-learning algorithm...Traditional machine-learning algorithms are struggling to handle the exceedingly large amount of data being generated by the internet. In real-world applications, there is an urgent need for machine-learning algorithms to be able to handle large-scale, high-dimensional text data. Cloud computing involves the delivery of computing and storage as a service to a heterogeneous community of recipients, Recently, it has aroused much interest in industry and academia. Most previous works on cloud platforms only focus on the parallel algorithms for structured data. In this paper, we focus on the parallel implementation of web-mining algorithms and develop a parallel web-mining system that includes parallel web crawler; parallel text extract, transform and load （ETL） and modeling; and parallel text mining and application subsystems. The complete system enables variable real-world web-mining applications for mass data.展开更多
The goal of this project is to use the Semantic Web Technologies and Data Mining for disease diagnosis to assist health care professionals regarding the possible medication and drug to prescribe (Drug recommendation) ...The goal of this project is to use the Semantic Web Technologies and Data Mining for disease diagnosis to assist health care professionals regarding the possible medication and drug to prescribe (Drug recommendation) according to the features of the patient. Numerous Decision Support Systems (DSS) and Expert Systems allow medical collaboration, like in the differential diagnosis specific or general. But, a medical recommendation system using both Semantic Web technologies and Data mining has not yet been developed which initiated this work. However, it should be mentioned that there are several system references about medicine or active ingredient interactions, but their final goal is not the Drug recommendation which uses above technologies. With this project we try to provide an assistant to the doctor for better recommendations. The patient will also able to use this system for explanation of drugs, food interaction and side effects of corresponding drugs.展开更多
Due to a great deal of valuable information contained in the Web log file, the result of Web mining can be used to enhance the decision making for electronic commerce (EC) operation and management. Because of ambiguo...Due to a great deal of valuable information contained in the Web log file, the result of Web mining can be used to enhance the decision making for electronic commerce (EC) operation and management. Because of ambiguous and abundance of the Web log file, the least decision making model based on rough set theory was presented for Web mining. And an example was given to explain the model. The model can predigest the decision making table, so that the least solution of the table can be acquired. According to the least solution, the corresponding decision for individual service can be made in sequence. Web mining based on rough set theory is also currently the original and particular method.展开更多
With the explosive growth of information sources available on the World Wide Web, how to combine the results of multiple search engines has become a valuable problem. In this paper, a search strategy based on genetic ...With the explosive growth of information sources available on the World Wide Web, how to combine the results of multiple search engines has become a valuable problem. In this paper, a search strategy based on genetic simulated annealing for search engines in Web mining is proposed. According to the proposed strategy, there exists some important relationship among Web statistical studies, search engines and optimization techniques. We have proven experimentally the relevance of our approach to the presented queries by comparing the qualities of output pages with those of the original downloaded pages, as the number of iterations increases better results are obtained with reasonable execution time.展开更多
Rough set theory is a new soft computing tool, and has received much attention of researchers around the world. It can deal with incomplete and uncertain information. Now, it has been applied in many areas successfull...Rough set theory is a new soft computing tool, and has received much attention of researchers around the world. It can deal with incomplete and uncertain information. Now, it has been applied in many areas successfully. This paper introduces the basic concepts of rough set and discusses its applications in Web mining. In particular, some applications of rough set theory to intelligent information processing are emphasized.展开更多
The backdoor or information leak of Web servers can be detected by using Web Mining techniques on some abnormal Web log and Web application log data. The security of Web servers can be enhanced and the damage of illeg...The backdoor or information leak of Web servers can be detected by using Web Mining techniques on some abnormal Web log and Web application log data. The security of Web servers can be enhanced and the damage of illegal access can be avoided. Firstly, the system for discovering the patterns of information leakages in CGI scripts from Web log data was proposed. Secondly, those patterns for system administrators to modify their codes and enhance their Web site security were provided. The following aspects were described: one is to combine web application log with web log to extract more information,so web data mining could be used to mine web log for discovering the information that firewall and Information Detection System cannot find. Another approach is to propose an operation module of web site to enhance Web site security. In cluster server session, Density -Based Clustering technique is used to reduce resource cost and obtain better efficiency.展开更多
The integration of the two fast-developing scientific research areas Semantic Web and Web Mining is known as Semantic Web Mining. The huge increase in the amount of Semantic Web data became a perfect target for many r...The integration of the two fast-developing scientific research areas Semantic Web and Web Mining is known as Semantic Web Mining. The huge increase in the amount of Semantic Web data became a perfect target for many researchers to apply Data Mining techniques on it. This paper gives a detailed state-of-the-art survey of on-going research in this new area. It shows the positive effects of Semantic Web Mining, the obstacles faced by researchers and propose number of approaches to deal with the very complex and heterogeneous information and knowledge which are produced by the technologies of Semantic Web.展开更多
As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major ca...As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommendation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on association rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the frequency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages that are not yet visited by users is not included in the recommendation set. To overcome this problem, we have used the web usage log in the adaptive association rule based web mining where the association rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.展开更多
Marburg virus disease(MVD)is a highly fatal illness,with a case fatality rate of up to 88%,though this rate can be significantly reduced with prompt and effective patient care.The disease was first identified in 1967 ...Marburg virus disease(MVD)is a highly fatal illness,with a case fatality rate of up to 88%,though this rate can be significantly reduced with prompt and effective patient care.The disease was first identified in 1967 during concurrent outbreaks in Marburg and Frankfurt,Germany,and in Belgrade,Serbia,linked to laboratory use of African green monkeys imported from Uganda.Subsequent outbreaks and isolated cases have been reported in various African countries,including Angola,the Democratic Republic of the Congo,Equatorial Guinea,Ghana,Guinea,Kenya,Rwanda,South Africa(in an individual with recent travel to Zimbabwe),Tanzania,and Uganda.Initial human MVD infections typically occur due to prolonged exposure to mines or caves inhabited by Rousettus aegyptiacus fruit bats,the natural hosts of the virus.展开更多
Researches on domestic social stability analysis mainly focus on construction of social stability theory,architecture and index,while few pay attention on quantitative analysis.In this paper,a social stability supervi...Researches on domestic social stability analysis mainly focus on construction of social stability theory,architecture and index,while few pay attention on quantitative analysis.In this paper,a social stability supervising framework is proposed based on sensitive Web information mining,semantic pattern matching and quantitative calculating.A sensitive information knowledge base is constructed by analyzing sensitive information about social environment,national harmonious and happy index of people’s live in natural language online news texts from Internet,and recognizing hot keywords as well as the event trends led by the keywords.A social stability index theoretic model and a quantitative calculating model are proposed to evaluate social stability quantitatively.Parameters of the calculating model are determined by employing social investigations and an iterative feedback learning method.A prototype system is built on proposed framework and experiments are conducted on six frontier provinces,e.g.,Xinjiang and Tibet.The result of an average accurate of 73.29%shows the effectiveness of the proposed model.展开更多
速度和效果是聚类算法面临的两大问题.DBSCAN(density based spatial clustering of applications with noise)是典型的基于密度的一种聚类方法,对于大型数据库的聚类实验显示了它在速度上的优越性.提出了一种基于密度的递归聚类算法(re...速度和效果是聚类算法面临的两大问题.DBSCAN(density based spatial clustering of applications with noise)是典型的基于密度的一种聚类方法,对于大型数据库的聚类实验显示了它在速度上的优越性.提出了一种基于密度的递归聚类算法(recursive density based clustering algorithm,简称RDBC),此算法可以智能地、动态地修改其密度参数.RDBC是基于DBSCAN的一种改进算法,其运算复杂度和DBSCAN相同.通过在Web文档上的聚类实验,结果表明,RDBC不但保留了DBSCAN高速度的优点,而且聚类效果大大优于DBSCAN.展开更多
Web-log contains a lot of information related with user activities on the Internet. How to mine user browsing interest patterns effectively is an important and challengeable research topic. On the analysis of the pres...Web-log contains a lot of information related with user activities on the Internet. How to mine user browsing interest patterns effectively is an important and challengeable research topic. On the analysis of the present algorithm’s advantages and disadvantages we propose a new concept: support-interest. Its key insight is that visitor will backtrack if they do not find the information where they expect. And the point from where they backtrack is the expected location for the page. We present User Access Matrix and the corresponding algorithm for discovering such expected locations that can handle page caching by the browser. Since the URL-URL matrix is a sparse matrix which can be represented by List of 3-tuples, we can mine user preferred sub-paths from the computation of this matrix. Accordingly, all the sub-paths are merged, and user preferred paths are formed. Experiments showed that it was accurate and scalable. It’s suitable for website based application, such as to optimize website’s topological structure or to design personalized services. Key words Web Mining - user preferred path - Web-log - support-interest - personalized services CLC number TP 391 Foundation item: Supported by the National High Technology Development (863 program of China) (2001AA113182)Biography: ZHOU Hong-fang (1976-), female.Ph. D candidate, research direction: data mining and knowledge discovery in databases.展开更多
文摘A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-com-merce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy data.This work pre-sents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)algorithm.The systolic tree is used for pattern mining,and based on this,the recommendations are given.The proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved.
基金supported by the National Natural Science Foundation of China (No. 61175052,60975039, 61203297, 60933004, 61035003)National High-tech R&D Program of China (863 Program) (No.2012AA011003)supported by the ZTE research found of Parallel Web Mining project
文摘Traditional machine-learning algorithms are struggling to handle the exceedingly large amount of data being generated by the internet. In real-world applications, there is an urgent need for machine-learning algorithms to be able to handle large-scale, high-dimensional text data. Cloud computing involves the delivery of computing and storage as a service to a heterogeneous community of recipients, Recently, it has aroused much interest in industry and academia. Most previous works on cloud platforms only focus on the parallel algorithms for structured data. In this paper, we focus on the parallel implementation of web-mining algorithms and develop a parallel web-mining system that includes parallel web crawler; parallel text extract, transform and load （ETL） and modeling; and parallel text mining and application subsystems. The complete system enables variable real-world web-mining applications for mass data.
文摘The goal of this project is to use the Semantic Web Technologies and Data Mining for disease diagnosis to assist health care professionals regarding the possible medication and drug to prescribe (Drug recommendation) according to the features of the patient. Numerous Decision Support Systems (DSS) and Expert Systems allow medical collaboration, like in the differential diagnosis specific or general. But, a medical recommendation system using both Semantic Web technologies and Data mining has not yet been developed which initiated this work. However, it should be mentioned that there are several system references about medicine or active ingredient interactions, but their final goal is not the Drug recommendation which uses above technologies. With this project we try to provide an assistant to the doctor for better recommendations. The patient will also able to use this system for explanation of drugs, food interaction and side effects of corresponding drugs.
文摘Due to a great deal of valuable information contained in the Web log file, the result of Web mining can be used to enhance the decision making for electronic commerce (EC) operation and management. Because of ambiguous and abundance of the Web log file, the least decision making model based on rough set theory was presented for Web mining. And an example was given to explain the model. The model can predigest the decision making table, so that the least solution of the table can be acquired. According to the least solution, the corresponding decision for individual service can be made in sequence. Web mining based on rough set theory is also currently the original and particular method.
基金Supported by the National Natural Science Foundation of China (60673093)
文摘With the explosive growth of information sources available on the World Wide Web, how to combine the results of multiple search engines has become a valuable problem. In this paper, a search strategy based on genetic simulated annealing for search engines in Web mining is proposed. According to the proposed strategy, there exists some important relationship among Web statistical studies, search engines and optimization techniques. We have proven experimentally the relevance of our approach to the presented queries by comparing the qualities of output pages with those of the original downloaded pages, as the number of iterations increases better results are obtained with reasonable execution time.
文摘Rough set theory is a new soft computing tool, and has received much attention of researchers around the world. It can deal with incomplete and uncertain information. Now, it has been applied in many areas successfully. This paper introduces the basic concepts of rough set and discusses its applications in Web mining. In particular, some applications of rough set theory to intelligent information processing are emphasized.
文摘The backdoor or information leak of Web servers can be detected by using Web Mining techniques on some abnormal Web log and Web application log data. The security of Web servers can be enhanced and the damage of illegal access can be avoided. Firstly, the system for discovering the patterns of information leakages in CGI scripts from Web log data was proposed. Secondly, those patterns for system administrators to modify their codes and enhance their Web site security were provided. The following aspects were described: one is to combine web application log with web log to extract more information,so web data mining could be used to mine web log for discovering the information that firewall and Information Detection System cannot find. Another approach is to propose an operation module of web site to enhance Web site security. In cluster server session, Density -Based Clustering technique is used to reduce resource cost and obtain better efficiency.
文摘The integration of the two fast-developing scientific research areas Semantic Web and Web Mining is known as Semantic Web Mining. The huge increase in the amount of Semantic Web data became a perfect target for many researchers to apply Data Mining techniques on it. This paper gives a detailed state-of-the-art survey of on-going research in this new area. It shows the positive effects of Semantic Web Mining, the obstacles faced by researchers and propose number of approaches to deal with the very complex and heterogeneous information and knowledge which are produced by the technologies of Semantic Web.
文摘As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommendation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on association rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the frequency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages that are not yet visited by users is not included in the recommendation set. To overcome this problem, we have used the web usage log in the adaptive association rule based web mining where the association rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.
文摘Marburg virus disease(MVD)is a highly fatal illness,with a case fatality rate of up to 88%,though this rate can be significantly reduced with prompt and effective patient care.The disease was first identified in 1967 during concurrent outbreaks in Marburg and Frankfurt,Germany,and in Belgrade,Serbia,linked to laboratory use of African green monkeys imported from Uganda.Subsequent outbreaks and isolated cases have been reported in various African countries,including Angola,the Democratic Republic of the Congo,Equatorial Guinea,Ghana,Guinea,Kenya,Rwanda,South Africa(in an individual with recent travel to Zimbabwe),Tanzania,and Uganda.Initial human MVD infections typically occur due to prolonged exposure to mines or caves inhabited by Rousettus aegyptiacus fruit bats,the natural hosts of the virus.
文摘Researches on domestic social stability analysis mainly focus on construction of social stability theory,architecture and index,while few pay attention on quantitative analysis.In this paper,a social stability supervising framework is proposed based on sensitive Web information mining,semantic pattern matching and quantitative calculating.A sensitive information knowledge base is constructed by analyzing sensitive information about social environment,national harmonious and happy index of people’s live in natural language online news texts from Internet,and recognizing hot keywords as well as the event trends led by the keywords.A social stability index theoretic model and a quantitative calculating model are proposed to evaluate social stability quantitatively.Parameters of the calculating model are determined by employing social investigations and an iterative feedback learning method.A prototype system is built on proposed framework and experiments are conducted on six frontier provinces,e.g.,Xinjiang and Tibet.The result of an average accurate of 73.29%shows the effectiveness of the proposed model.
文摘速度和效果是聚类算法面临的两大问题.DBSCAN(density based spatial clustering of applications with noise)是典型的基于密度的一种聚类方法,对于大型数据库的聚类实验显示了它在速度上的优越性.提出了一种基于密度的递归聚类算法(recursive density based clustering algorithm,简称RDBC),此算法可以智能地、动态地修改其密度参数.RDBC是基于DBSCAN的一种改进算法,其运算复杂度和DBSCAN相同.通过在Web文档上的聚类实验,结果表明,RDBC不但保留了DBSCAN高速度的优点,而且聚类效果大大优于DBSCAN.
文摘Web-log contains a lot of information related with user activities on the Internet. How to mine user browsing interest patterns effectively is an important and challengeable research topic. On the analysis of the present algorithm’s advantages and disadvantages we propose a new concept: support-interest. Its key insight is that visitor will backtrack if they do not find the information where they expect. And the point from where they backtrack is the expected location for the page. We present User Access Matrix and the corresponding algorithm for discovering such expected locations that can handle page caching by the browser. Since the URL-URL matrix is a sparse matrix which can be represented by List of 3-tuples, we can mine user preferred sub-paths from the computation of this matrix. Accordingly, all the sub-paths are merged, and user preferred paths are formed. Experiments showed that it was accurate and scalable. It’s suitable for website based application, such as to optimize website’s topological structure or to design personalized services. Key words Web Mining - user preferred path - Web-log - support-interest - personalized services CLC number TP 391 Foundation item: Supported by the National High Technology Development (863 program of China) (2001AA113182)Biography: ZHOU Hong-fang (1976-), female.Ph. D candidate, research direction: data mining and knowledge discovery in databases.
