A new method for Web users fuzzy clustering based on analysis of user interest characteristic is proposed in this article. The method first defines page fuzzy categories according to the links on the index page of the...A new method for Web users fuzzy clustering based on analysis of user interest characteristic is proposed in this article. The method first defines page fuzzy categories according to the links on the index page of the site, then computes fuzzy degree of cross page through aggregating on data of Web log. After that, by using fuzzy comprehensive evaluation method, the method constructs user interest vectors according to page viewing times and frequency of hits, and derives the fuzzy similarity matrix from the interest vectors for the Web users. Finally, it gets the clustering result through the fuzzy clustering method. The experimental results show the effectiveness of the method. Key words Web log mining - fuzzy similarity matrix - fuzzy comprehensive evaluation - fuzzy clustering CLC number TP18 - TP311 - TP391 Foundation item: Supported by the Natural Science Foundation of Heilongjiang Province of China (F0304)Biography: ZHAN Li-qiang (1966-), male, Lecturer, Ph. D. research direction: the theory methods of data mining and theory of database.展开更多
Due to the increase in the number of smart meter devices,a power grid generates a large amount of data.Analyzing the data can help in understanding the users’electricity consumption behavior and demands;thus,enabling...Due to the increase in the number of smart meter devices,a power grid generates a large amount of data.Analyzing the data can help in understanding the users’electricity consumption behavior and demands;thus,enabling better service to be provided to them.Performing power load profile clustering is the basis for mining the users’electricity consumption behavior.By examining the complexity,randomness,and uncertainty of the users’electricity consumption behavior,this paper proposes an ensemble clustering method to analyze this behavior.First,principle component analysis(PCA)is used to reduce the dimensions of the data.Subsequently,the single clustering method is used,and the majority is selected for integrated clustering.As a result,the users’electricity consumption behavior is classified into different modes,and their characteristics are analyzed in detail.This paper examines the electricity power data of 19 real users in China for simulation purposes.This manuscript provides a thorough analysis along with suggestions for the users’weekly electricity consumption behavior.The results verify the effectiveness of the proposed method.展开更多
In this paper, an improved algorithm, named STC-I, is proposed for Chinese Web page clustering based on Chinese language characteristics, which adopts a new unit choice principle and a novel suffix tree construction p...In this paper, an improved algorithm, named STC-I, is proposed for Chinese Web page clustering based on Chinese language characteristics, which adopts a new unit choice principle and a novel suffix tree construction policy. The experimental results show that the new algorithm keeps advantages of STC, and is better than STC in precision and speed when they are used to cluster Chinese Web page. Key words clustering - suffix tree - Web mining CLC number TP 311 Foundation item: Supported by the National Information Industry Development Foundation of ChinaBiography: YANG Jian-wu (1973-), male, Ph. D, research direction: information retrieval and text mining.展开更多
The cloud computing has been growing over the past few years, and service providers are creating an intense competitive world of business. This proliferation makes it hard for new users to select a proper service amon...The cloud computing has been growing over the past few years, and service providers are creating an intense competitive world of business. This proliferation makes it hard for new users to select a proper service among a large amount of service candidates. A novel user preferences-aware recommendation approach for trustworthy services is presented. For describing the requirements of new users in different application scenarios, user preferences are identified by usage preference, trust preference and cost preference. According to the similarity analysis of usage preference between consumers and new users, the candidates are selected, and these data about service trust provided by them are calculated as the fuzzy comprehensive evaluations. In accordance with the trust and cost preferences of new users, the dynamic fuzzy clusters are generated based on the fuzzy similarity computation. Then, the most suitable services can be selected to recommend to new users. The experiments show that this approach is effective and feasible, and can improve the quality of services recommendation meeting the requirements of new users in different scenario.展开更多
This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering.Firstly,we construct a set of points which are composed of spatial location pixels and gray levels fro...This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering.Firstly,we construct a set of points which are composed of spatial location pixels and gray levels from a given image.Secondly,the data is clustered in spectral space of the similar matrix of the set points,in order to avoid the drawbacks of K-means algorithm in the conventional spectral clustering method that is sensitive to initial clustering centroids and convergence to local optimal solution,we introduce the clone operator,Cauthy mutation to enlarge the scale of clustering centers,quantum-inspired evolutionary algorithm to find the global optimal clustering centroids.Compared with phishing web image segmentation based on K-means,experimental results show that the segmentation performance of our method gains much improvement.Moreover,our method can convergence to global optimal solution and is better in accuracy of phishing web segmentation.展开更多
ultra-Dense Network(UDN)has been envisioned as a promising technology to provide high-quality wireless connectivity in dense urban areas,in which the density of Access Points(APs)is increased up to the point where it ...ultra-Dense Network(UDN)has been envisioned as a promising technology to provide high-quality wireless connectivity in dense urban areas,in which the density of Access Points(APs)is increased up to the point where it is comparable with or surpasses the density of active mobile users.In order to mitigate inter-AP interference and improve spectrum efficiency,APs in UDNs are usually clustered into multiple groups to serve different mobile users,respectively.However,as the number of APs increases,the computational capability within an AP group has become the bottleneck of AP clustering.In this paper,we first propose a novel UDN architecture based on Mobile Edge Computing(MEC),in which each MEC server is associated with a user-centric AP cluster to act as a mobile agent.In addition,in the context of MEC-based UDN,we leverage mobility prediction techniques to achieve a dynamic AP clustering scheme,in which the cluster structure can automatically adapt to the dynamic distribution of user traffic in a specific area.Simulation results show that the proposed scheme can highly increase the average user throughput compared with the baseline algorithm using max-SINR user association and equal bandwidth allocation,while it guarantees at the same time low transmission delay.展开更多
Non-orthogonal multiple access(NOMA)has been a key enabling technology for the fifth generation(5G)cellular networks.Based on the NOMA principle,a traditional neural network has been implemented for user clustering(UC...Non-orthogonal multiple access(NOMA)has been a key enabling technology for the fifth generation(5G)cellular networks.Based on the NOMA principle,a traditional neural network has been implemented for user clustering(UC)to maximize the NOMA system’s throughput performance by considering that each sample is independent of the prior and the subsequent ones.Consequently,the prediction of UC for the future ones is based on the current clustering information,which is never used again due to the lack of memory of the network.Therefore,to relate the input features of NOMA users and capture the dependency in the clustering information,time-series methods can assist us in gaining a helpful insight into the future.Despite its mathematical complexity,the essence of time series comes down to examining past behavior and extending that information into the future.Hence,in this paper,we propose a novel and effective stacked long short term memory(S-LSTM)to predict the UC formation of NOMA users to enhance the throughput performance of the 5G-based NOMA systems.In the proposed strategy,the S-LSTM is modelled to handle the time-series input data to improve the predicting accuracy of UC of the NOMA users by implementing multiple LSTM layers with hidden cells.The implemented LSTM layers have feedback connections that help to capture the dependency in the clustering information as it propagates between the layers.Specifically,we develop,train,validate and test the proposed model to predict the UC formation for the futures ones by capturing the dependency in the clustering information based on the time-series data.Simulation results demonstrate that the proposed scheme effectively predicts UC and thereby attaining near-optimal throughput performance of 98.94%compared to the exhaustive search method.展开更多
In this paper, we present a novel approach to model user request patterns in the World Wide Web. Instead of focusing on the user traffic for web pages, we capture the user interaction at the object level of the web pa...In this paper, we present a novel approach to model user request patterns in the World Wide Web. Instead of focusing on the user traffic for web pages, we capture the user interaction at the object level of the web pages. Our framework model consists of three sub-models: one for user file access, one for web pages, and one for storage servers. Web pages are assumed to consist of different types and sizes of objects, which are characterized using several categories: articles, media, and mosaics. The model is implemented with a discrete event simulation and then used to investigate the performance of our system over a variety of parameters in our model. Our performance measure of choice is mean response time and by varying the composition of web pages through our categories, we find that our framework model is able to capture a wide range of conditions that serve as a basis for generating a variety of user request patterns. In addition, we are able to establish a set of parameters that can be used as base cases. One of the goals of this research is for the framework model to be general enough that the parameters can be varied such that it can serve as input for investigating other distributed applications that require the generation of user request access patterns.展开更多
Conceptual clustering is mainly used for solving the deficiency and incompleteness of domain knowledge. Based on conceptual clustering technology and aiming at the institutional framework and characteristic of Web the...Conceptual clustering is mainly used for solving the deficiency and incompleteness of domain knowledge. Based on conceptual clustering technology and aiming at the institutional framework and characteristic of Web theme information, this paper proposes and implements dynamic conceptual clustering algorithm and merging algorithm for Web documents, and also analyses the super performance of the clustering algorithm in efficiency and clustering accuracy. Key words conceptual clustering - clustering center - dynamic conceptual clustering - theme - web documents clustering CLC number TP 311 Foundation item: Supported by the National “863” Program of China (2002AA111010, 2003AA001032)Biography: WANG Yun-hua(1979-), male, Master candidate, research direction: knowledge engineering and data mining.展开更多
We combine the web usage mining and fuzzy clustering and give the concept of web fuzzy clustering, and then put forward the web fuzzy clustering processing model which is discussed in detail. Web fuzzy clustering can ...We combine the web usage mining and fuzzy clustering and give the concept of web fuzzy clustering, and then put forward the web fuzzy clustering processing model which is discussed in detail. Web fuzzy clustering can be used in the web users clustering and web pages clustering. In the end, a case study is given and the result has proved the feasibility of using web fuzzy clustering in web pages clustering. Key words web mining - web usage mining - web fuzzy clustering - WFCM CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (90104005)Biography: LIU Mao-fu (1977-), male, Ph. D candidate, research direction: artificial intelligence, web mining, image mining.展开更多
To alleviate the scalability problem caused by the increasing Web using and changing users＇ interests, this paper presents a novel Web Usage Mining algorithm-Incremental Web Usage Mining algorithm based on Active Ant...To alleviate the scalability problem caused by the increasing Web using and changing users＇ interests, this paper presents a novel Web Usage Mining algorithm-Incremental Web Usage Mining algorithm based on Active Ant Colony Clustering. Firstly, an active movement strategy about direction selection and speed, different with the positive strategy employed by other Ant Colony Clustering algorithms, is proposed to construct an Active Ant Colony Clustering algorithm, which avoid the idle and ＂flying over the plane＂ moving phenomenon, effectively improve the quality and speed of clustering on large dataset. Then a mechanism of decomposing clusters based on above methods is introduced to form new clusters when users＇ interests change. Empirical studies on a real Web dataset show the active ant colony clustering algorithm has better performance than the previous algorithms, and the incremental approach based on the proposed mechanism can efficiently implement incremental Web usage mining.展开更多
The content-ignorant clustering method takes advantages in time complexity and space complexity than the content based methods.In this paper,the authors introduce a unified expanding method for content-ignorant web pa...The content-ignorant clustering method takes advantages in time complexity and space complexity than the content based methods.In this paper,the authors introduce a unified expanding method for content-ignorant web page clustering by mining the "click-through" log,which tries to solve the problem that the "click-through" log is sparse.The relationship between two nodes which have been expanded is also defined and optimized.Analysis and experiment show that the performance of the new method has improved,by the comparison with the standard content-ignorant method.The new method can also work without iterative clustering.展开更多
User model which is the representation of information about user is the heart of adaptive systems. It helps adaptive systems to perform adaptation tasks. There are two kinds of adaptations: 1) Individual adaptation re...User model which is the representation of information about user is the heart of adaptive systems. It helps adaptive systems to perform adaptation tasks. There are two kinds of adaptations: 1) Individual adaptation regarding to each user;2) Group adaptation focusing on group of users. To support group adaptation, the basic problem which needs to be solved is how to create user groups. This relates to clustering techniques so as to cluster user models because a group is considered as a cluster of similar user models. In this paper we discuss two clustering algorithms: k-means and k-medoids and also propose dissimilarity measures and similarity measures which are applied into different structures (forms) of user models like vector, overlay, and Bayesian network.展开更多
Corporations focus on web based education to train their employees ever more than before. Unlike traditional learning environments, web based education applications store large amount of data. This growing availabilit...Corporations focus on web based education to train their employees ever more than before. Unlike traditional learning environments, web based education applications store large amount of data. This growing availability of data stimulated the emergence of a new field called educational data mining. In this study, the classification method is implemented on a data that is obtained from a company which uses web based education to train their employees. The authors＇ aim is to find out the most critical factors that influence the users＇ success. For the classification of the data, two decision tree algorithms, Classification and Regression Tree （CART） and Quick, Unbiased and Efficient Statistical Tree （QUEST） are applied. According to the results, assurance of a certificate at the end of the training is found to be the most critical factor that influences the users＇ success. Position, number of work years and the education level of the user, are also found as important factors.展开更多
The paper focuses on the habits of China Web users' language utilization behaviors in accessing the Web. It also seeks to make a general study on the basic nature of language phenomenon with regard to digital acce...The paper focuses on the habits of China Web users' language utilization behaviors in accessing the Web. It also seeks to make a general study on the basic nature of language phenomenon with regard to digital accessing. A questionnaire survey was formulated and distributed online for these research purposes. There were 1,267 responses collected. The data were analyzed with descriptive statistics, Chi-square testing and contingency table analyses. Results revealed the following findings. Tagging has already played an important role in Web2.0 communication for China's Web users. China users rely greatly on all kinds of taxonomies in browsing and have also an awareness of them in effective searching. These imply that the classified languages in digital environment may aid Chinese Web users in a more satisfying manner. Highly subject-specific words, especially those from authorized tools, yielded better results in searching. Chinese users have high recognition for related terms. As to the demographic aspect, there is little difference between different genders in the utilization of information retrieval languages. Age may constitute a variable element to a certain degree. Educational background has a complex effect on language utilizations in searching. These research findings characterize China Web users' behaviors in digital information accessing. They also can be potentially valuable for the modeling and further refinement of digital accessing services.展开更多
Optimal clustering for the web documents is known to complicated cornbinatorial Optimization problem and it is hard to develop a generally applicable oplimal algorithm. An accelerated simuIated arlneaIing aIgorithm is...Optimal clustering for the web documents is known to complicated cornbinatorial Optimization problem and it is hard to develop a generally applicable oplimal algorithm. An accelerated simuIated arlneaIing aIgorithm is developed for automatic web document classification. The web document classification problem is addressed as the problem of best describing a match between a web query and a hypothesized web object. The normalized term frequency and inverse document frequency coefficient is used as a measure of the match. Test beds are generated on - line during the search by transforming model web sites. As a result, web sites can be clustered optimally in terms of keyword vectofs of corresponding web documents.展开更多
In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web page...In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web pages in accordance with user preferences is proposed.PWPR assigns the initial weights based on user interests and creates the virtual links and hubs according to user interests.By measuring user click streams,PWPR incrementally reflects users’ favors for the personalized ranking.To improve the accuracy of ranking, PWPR also takes collaborative filtering into consideration when the query with similar is submitted by users who have similar user interests. Detailed simulation results and comparison with other algorithms prove that the proposed PWPR can adaptively provide personalized ranking and truly relevant information to user preferences.展开更多
文摘A new method for Web users fuzzy clustering based on analysis of user interest characteristic is proposed in this article. The method first defines page fuzzy categories according to the links on the index page of the site, then computes fuzzy degree of cross page through aggregating on data of Web log. After that, by using fuzzy comprehensive evaluation method, the method constructs user interest vectors according to page viewing times and frequency of hits, and derives the fuzzy similarity matrix from the interest vectors for the Web users. Finally, it gets the clustering result through the fuzzy clustering method. The experimental results show the effectiveness of the method. Key words Web log mining - fuzzy similarity matrix - fuzzy comprehensive evaluation - fuzzy clustering CLC number TP18 - TP311 - TP391 Foundation item: Supported by the Natural Science Foundation of Heilongjiang Province of China (F0304)Biography: ZHAN Li-qiang (1966-), male, Lecturer, Ph. D. research direction: the theory methods of data mining and theory of database.
基金supported by the State Grid Science and Technology Project (No.5442AI90009)Natural Science Foundation of China (No. 6170337)
文摘Due to the increase in the number of smart meter devices,a power grid generates a large amount of data.Analyzing the data can help in understanding the users’electricity consumption behavior and demands;thus,enabling better service to be provided to them.Performing power load profile clustering is the basis for mining the users’electricity consumption behavior.By examining the complexity,randomness,and uncertainty of the users’electricity consumption behavior,this paper proposes an ensemble clustering method to analyze this behavior.First,principle component analysis(PCA)is used to reduce the dimensions of the data.Subsequently,the single clustering method is used,and the majority is selected for integrated clustering.As a result,the users’electricity consumption behavior is classified into different modes,and their characteristics are analyzed in detail.This paper examines the electricity power data of 19 real users in China for simulation purposes.This manuscript provides a thorough analysis along with suggestions for the users’weekly electricity consumption behavior.The results verify the effectiveness of the proposed method.
文摘In this paper, an improved algorithm, named STC-I, is proposed for Chinese Web page clustering based on Chinese language characteristics, which adopts a new unit choice principle and a novel suffix tree construction policy. The experimental results show that the new algorithm keeps advantages of STC, and is better than STC in precision and speed when they are used to cluster Chinese Web page. Key words clustering - suffix tree - Web mining CLC number TP 311 Foundation item: Supported by the National Information Industry Development Foundation of ChinaBiography: YANG Jian-wu (1973-), male, Ph. D, research direction: information retrieval and text mining.
基金Project(61272148) supported by the National Natural Science Foundation of ChinaProject(2014FJ3122) supported by the Science and Technology Project of Hunan Province,China
文摘The cloud computing has been growing over the past few years, and service providers are creating an intense competitive world of business. This proliferation makes it hard for new users to select a proper service among a large amount of service candidates. A novel user preferences-aware recommendation approach for trustworthy services is presented. For describing the requirements of new users in different application scenarios, user preferences are identified by usage preference, trust preference and cost preference. According to the similarity analysis of usage preference between consumers and new users, the candidates are selected, and these data about service trust provided by them are calculated as the fuzzy comprehensive evaluations. In accordance with the trust and cost preferences of new users, the dynamic fuzzy clusters are generated based on the fuzzy similarity computation. Then, the most suitable services can be selected to recommend to new users. The experiments show that this approach is effective and feasible, and can improve the quality of services recommendation meeting the requirements of new users in different scenario.
基金Supported by the Fundamental Research Funds for the Central Universities in North China Electric Power University(11MG13)the Natural Science Foundation of Hebei Province(F2011502038)
文摘This paper proposes a novel phishing web image segmentation algorithm which based on improving spectral clustering.Firstly,we construct a set of points which are composed of spatial location pixels and gray levels from a given image.Secondly,the data is clustered in spectral space of the similar matrix of the set points,in order to avoid the drawbacks of K-means algorithm in the conventional spectral clustering method that is sensitive to initial clustering centroids and convergence to local optimal solution,we introduce the clone operator,Cauthy mutation to enlarge the scale of clustering centers,quantum-inspired evolutionary algorithm to find the global optimal clustering centroids.Compared with phishing web image segmentation based on K-means,experimental results show that the segmentation performance of our method gains much improvement.Moreover,our method can convergence to global optimal solution and is better in accuracy of phishing web segmentation.
基金This work was partially supported by the National Natural Science Foundation of China(61801208,61671233,61931023)the Jiangsu Science Foundation(BK20170650)+2 种基金the Postdoctoral Science Foundation of China(BX201700118,2017M621712)the Jiangsu Postdoctoral Science Foundation(1701118B)the open research fund of National Mobile Communications Research Laboratory(2019D02).
文摘ultra-Dense Network(UDN)has been envisioned as a promising technology to provide high-quality wireless connectivity in dense urban areas,in which the density of Access Points(APs)is increased up to the point where it is comparable with or surpasses the density of active mobile users.In order to mitigate inter-AP interference and improve spectrum efficiency,APs in UDNs are usually clustered into multiple groups to serve different mobile users,respectively.However,as the number of APs increases,the computational capability within an AP group has become the bottleneck of AP clustering.In this paper,we first propose a novel UDN architecture based on Mobile Edge Computing(MEC),in which each MEC server is associated with a user-centric AP cluster to act as a mobile agent.In addition,in the context of MEC-based UDN,we leverage mobility prediction techniques to achieve a dynamic AP clustering scheme,in which the cluster structure can automatically adapt to the dynamic distribution of user traffic in a specific area.Simulation results show that the proposed scheme can highly increase the average user throughput compared with the baseline algorithm using max-SINR user association and equal bandwidth allocation,while it guarantees at the same time low transmission delay.
基金This work was funded by Multimedia University under Grant Number MMUI/170084.
文摘Non-orthogonal multiple access(NOMA)has been a key enabling technology for the fifth generation(5G)cellular networks.Based on the NOMA principle,a traditional neural network has been implemented for user clustering(UC)to maximize the NOMA system’s throughput performance by considering that each sample is independent of the prior and the subsequent ones.Consequently,the prediction of UC for the future ones is based on the current clustering information,which is never used again due to the lack of memory of the network.Therefore,to relate the input features of NOMA users and capture the dependency in the clustering information,time-series methods can assist us in gaining a helpful insight into the future.Despite its mathematical complexity,the essence of time series comes down to examining past behavior and extending that information into the future.Hence,in this paper,we propose a novel and effective stacked long short term memory(S-LSTM)to predict the UC formation of NOMA users to enhance the throughput performance of the 5G-based NOMA systems.In the proposed strategy,the S-LSTM is modelled to handle the time-series input data to improve the predicting accuracy of UC of the NOMA users by implementing multiple LSTM layers with hidden cells.The implemented LSTM layers have feedback connections that help to capture the dependency in the clustering information as it propagates between the layers.Specifically,we develop,train,validate and test the proposed model to predict the UC formation for the futures ones by capturing the dependency in the clustering information based on the time-series data.Simulation results demonstrate that the proposed scheme effectively predicts UC and thereby attaining near-optimal throughput performance of 98.94%compared to the exhaustive search method.
文摘In this paper, we present a novel approach to model user request patterns in the World Wide Web. Instead of focusing on the user traffic for web pages, we capture the user interaction at the object level of the web pages. Our framework model consists of three sub-models: one for user file access, one for web pages, and one for storage servers. Web pages are assumed to consist of different types and sizes of objects, which are characterized using several categories: articles, media, and mosaics. The model is implemented with a discrete event simulation and then used to investigate the performance of our system over a variety of parameters in our model. Our performance measure of choice is mean response time and by varying the composition of web pages through our categories, we find that our framework model is able to capture a wide range of conditions that serve as a basis for generating a variety of user request patterns. In addition, we are able to establish a set of parameters that can be used as base cases. One of the goals of this research is for the framework model to be general enough that the parameters can be varied such that it can serve as input for investigating other distributed applications that require the generation of user request access patterns.
文摘Conceptual clustering is mainly used for solving the deficiency and incompleteness of domain knowledge. Based on conceptual clustering technology and aiming at the institutional framework and characteristic of Web theme information, this paper proposes and implements dynamic conceptual clustering algorithm and merging algorithm for Web documents, and also analyses the super performance of the clustering algorithm in efficiency and clustering accuracy. Key words conceptual clustering - clustering center - dynamic conceptual clustering - theme - web documents clustering CLC number TP 311 Foundation item: Supported by the National “863” Program of China (2002AA111010, 2003AA001032)Biography: WANG Yun-hua(1979-), male, Master candidate, research direction: knowledge engineering and data mining.
文摘We combine the web usage mining and fuzzy clustering and give the concept of web fuzzy clustering, and then put forward the web fuzzy clustering processing model which is discussed in detail. Web fuzzy clustering can be used in the web users clustering and web pages clustering. In the end, a case study is given and the result has proved the feasibility of using web fuzzy clustering in web pages clustering. Key words web mining - web usage mining - web fuzzy clustering - WFCM CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (90104005)Biography: LIU Mao-fu (1977-), male, Ph. D candidate, research direction: artificial intelligence, web mining, image mining.
基金Supported by the Natural Science Foundation of Jiangsu Province(BK2005046)
文摘To alleviate the scalability problem caused by the increasing Web using and changing users＇ interests, this paper presents a novel Web Usage Mining algorithm-Incremental Web Usage Mining algorithm based on Active Ant Colony Clustering. Firstly, an active movement strategy about direction selection and speed, different with the positive strategy employed by other Ant Colony Clustering algorithms, is proposed to construct an Active Ant Colony Clustering algorithm, which avoid the idle and ＂flying over the plane＂ moving phenomenon, effectively improve the quality and speed of clustering on large dataset. Then a mechanism of decomposing clusters based on above methods is introduced to form new clusters when users＇ interests change. Empirical studies on a real Web dataset show the active ant colony clustering algorithm has better performance than the previous algorithms, and the incremental approach based on the proposed mechanism can efficiently implement incremental Web usage mining.
文摘The content-ignorant clustering method takes advantages in time complexity and space complexity than the content based methods.In this paper,the authors introduce a unified expanding method for content-ignorant web page clustering by mining the "click-through" log,which tries to solve the problem that the "click-through" log is sparse.The relationship between two nodes which have been expanded is also defined and optimized.Analysis and experiment show that the performance of the new method has improved,by the comparison with the standard content-ignorant method.The new method can also work without iterative clustering.
文摘User model which is the representation of information about user is the heart of adaptive systems. It helps adaptive systems to perform adaptation tasks. There are two kinds of adaptations: 1) Individual adaptation regarding to each user;2) Group adaptation focusing on group of users. To support group adaptation, the basic problem which needs to be solved is how to create user groups. This relates to clustering techniques so as to cluster user models because a group is considered as a cluster of similar user models. In this paper we discuss two clustering algorithms: k-means and k-medoids and also propose dissimilarity measures and similarity measures which are applied into different structures (forms) of user models like vector, overlay, and Bayesian network.
文摘Corporations focus on web based education to train their employees ever more than before. Unlike traditional learning environments, web based education applications store large amount of data. This growing availability of data stimulated the emergence of a new field called educational data mining. In this study, the classification method is implemented on a data that is obtained from a company which uses web based education to train their employees. The authors＇ aim is to find out the most critical factors that influence the users＇ success. For the classification of the data, two decision tree algorithms, Classification and Regression Tree （CART） and Quick, Unbiased and Efficient Statistical Tree （QUEST） are applied. According to the results, assurance of a certificate at the end of the training is found to be the most critical factor that influences the users＇ success. Position, number of work years and the education level of the user, are also found as important factors.
文摘The paper focuses on the habits of China Web users' language utilization behaviors in accessing the Web. It also seeks to make a general study on the basic nature of language phenomenon with regard to digital accessing. A questionnaire survey was formulated and distributed online for these research purposes. There were 1,267 responses collected. The data were analyzed with descriptive statistics, Chi-square testing and contingency table analyses. Results revealed the following findings. Tagging has already played an important role in Web2.0 communication for China's Web users. China users rely greatly on all kinds of taxonomies in browsing and have also an awareness of them in effective searching. These imply that the classified languages in digital environment may aid Chinese Web users in a more satisfying manner. Highly subject-specific words, especially those from authorized tools, yielded better results in searching. Chinese users have high recognition for related terms. As to the demographic aspect, there is little difference between different genders in the utilization of information retrieval languages. Age may constitute a variable element to a certain degree. Educational background has a complex effect on language utilizations in searching. These research findings characterize China Web users' behaviors in digital information accessing. They also can be potentially valuable for the modeling and further refinement of digital accessing services.
文摘Optimal clustering for the web documents is known to complicated cornbinatorial Optimization problem and it is hard to develop a generally applicable oplimal algorithm. An accelerated simuIated arlneaIing aIgorithm is developed for automatic web document classification. The web document classification problem is addressed as the problem of best describing a match between a web query and a hypothesized web object. The normalized term frequency and inverse document frequency coefficient is used as a measure of the match. Test beds are generated on - line during the search by transforming model web sites. As a result, web sites can be clustered optimally in terms of keyword vectofs of corresponding web documents.
基金The Natural Science Foundation of South-Central University for Nationalities(No.YZZ07006)
文摘In order to rank searching results according to the user preferences,a new personalized web pages ranking algorithm called PWPR（personalized web page ranking）with the idea of adjusting the ranking scores of web pages in accordance with user preferences is proposed.PWPR assigns the initial weights based on user interests and creates the virtual links and hubs according to user interests.By measuring user click streams,PWPR incrementally reflects users’ favors for the personalized ranking.To improve the accuracy of ranking, PWPR also takes collaborative filtering into consideration when the query with similar is submitted by users who have similar user interests. Detailed simulation results and comparison with other algorithms prove that the proposed PWPR can adaptively provide personalized ranking and truly relevant information to user preferences.
