The Experts below are selected from a list of 129 Experts worldwide ranked by ideXlab platform
Andrea Tagarelli - One of the best experts on this subject based on the ideXlab platform.
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PKDD - A tree-based approach to Clustering XML documents by structure
Lecture Notes in Computer Science, 2004Co-Authors: Gianni Costa, Giuseppe Manco, Riccardo Ortale, Andrea TagarelliAbstract:We propose a novel methodology for Clustering XML documents on the basis of their structural similarities. The idea is to equip each Cluster with an XML Cluster Representative, i.e. an XML document subsuming the most typical structural specifics of a set of XML documents. Clustering is essentially accomplished by comparing Cluster Representatives, and updating the Representatives as soon as new Clusters are detected. We present an algorithm for the computation of an XML Representative based on suitable techniques for identifying significant node matchings and for reliably merging and pruning XML trees. Experimental evaluation performed on both synthetic and real data shows the effectiveness of our approach.
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Distance-based Clustering of XML Documents
2003Co-Authors: Francesco De Francesca, Riccardo Ortale, Gianluca Gordano, Andrea TagarelliAbstract:The increasing relevance of the Web as a mean for sharing information around the world has posed several new interesting issues to the computer science research community. The traditional approaches to information handling are ineffective in the new context: they are mainly devoted to the management of highly structured information, like relational databases, whereas Web data are semistructured and encoded using different formats (HTML, XML, and so on). In such context, we address the problem of Clustering structurally similar Web documents, and in particular XML documents. This problem has several interesting applications, related, e.g., to the management of Web data. For example, the detection of structural similarities among documents can help in solving the problem of recognizing different sources providing the same kind of information [2], or in the structural analysis of a Web site. In this paper we propose a novel methodology for Clustering XML documents, focusing on the notion of XML Cluster Representative, i.e., a prototype XML document subsuming the most relevant features of the set of XML documents within the Cluster. In particular, we devise a technique to compute a Representative of a set of XML documents, which is capable of capturing all the structural specificities within the represented documents. To this purpose, the notion of structural matching between the trees associated to two XML documents is exploited. Structural matchings allow to both identify the structural similarities between two XML documents and to build a Representative around these similarities. We also investigate the exploitation of merging and pruning strategies for refining XML document trees into effective Cluster Representatives.
Zhou Hao - One of the best experts on this subject based on the ideXlab platform.
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Product Diversification,Alliance Marketing and Team Marketing of Cluster—Representative Model of Shantou Chenghai
Modern Economic Science, 2006Co-Authors: Zhou HaoAbstract:This article constructs a model by introducing product differentiation into the Salop model and analyzes the two forms of Clusters(alliance marketing and team marketing) with extended model,exampled by the Representative(jian ke zhi) in Chenghai of Shantou.The main conclusion is that Cluster marketing is related to product differentiation.Marketing form of Cluster evolves as the product differentiation.Manufacturers prefer alliance marketing when the product differentiation is relatively large.Otherwise,team marketing preferred to alliance marketing.In addition,the article proposes the optimal size of team marketing.
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product diversification alliance marketing and team marketing of Cluster Representative model of shantou chenghai
Modern Economic Science, 2006Co-Authors: Zhou HaoAbstract:This article constructs a model by introducing product differentiation into the Salop model and analyzes the two forms of Clusters(alliance marketing and team marketing) with extended model,exampled by the Representative(jian ke zhi) in Chenghai of Shantou.The main conclusion is that Cluster marketing is related to product differentiation.Marketing form of Cluster evolves as the product differentiation.Manufacturers prefer alliance marketing when the product differentiation is relatively large.Otherwise,team marketing preferred to alliance marketing.In addition,the article proposes the optimal size of team marketing.
Gianni Costa - One of the best experts on this subject based on the ideXlab platform.
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PKDD - A tree-based approach to Clustering XML documents by structure
Lecture Notes in Computer Science, 2004Co-Authors: Gianni Costa, Giuseppe Manco, Riccardo Ortale, Andrea TagarelliAbstract:We propose a novel methodology for Clustering XML documents on the basis of their structural similarities. The idea is to equip each Cluster with an XML Cluster Representative, i.e. an XML document subsuming the most typical structural specifics of a set of XML documents. Clustering is essentially accomplished by comparing Cluster Representatives, and updating the Representatives as soon as new Clusters are detected. We present an algorithm for the computation of an XML Representative based on suitable techniques for identifying significant node matchings and for reliably merging and pruning XML trees. Experimental evaluation performed on both synthetic and real data shows the effectiveness of our approach.
Riccardo Ortale - One of the best experts on this subject based on the ideXlab platform.
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PKDD - A tree-based approach to Clustering XML documents by structure
Lecture Notes in Computer Science, 2004Co-Authors: Gianni Costa, Giuseppe Manco, Riccardo Ortale, Andrea TagarelliAbstract:We propose a novel methodology for Clustering XML documents on the basis of their structural similarities. The idea is to equip each Cluster with an XML Cluster Representative, i.e. an XML document subsuming the most typical structural specifics of a set of XML documents. Clustering is essentially accomplished by comparing Cluster Representatives, and updating the Representatives as soon as new Clusters are detected. We present an algorithm for the computation of an XML Representative based on suitable techniques for identifying significant node matchings and for reliably merging and pruning XML trees. Experimental evaluation performed on both synthetic and real data shows the effectiveness of our approach.
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Distance-based Clustering of XML Documents
2003Co-Authors: Francesco De Francesca, Riccardo Ortale, Gianluca Gordano, Andrea TagarelliAbstract:The increasing relevance of the Web as a mean for sharing information around the world has posed several new interesting issues to the computer science research community. The traditional approaches to information handling are ineffective in the new context: they are mainly devoted to the management of highly structured information, like relational databases, whereas Web data are semistructured and encoded using different formats (HTML, XML, and so on). In such context, we address the problem of Clustering structurally similar Web documents, and in particular XML documents. This problem has several interesting applications, related, e.g., to the management of Web data. For example, the detection of structural similarities among documents can help in solving the problem of recognizing different sources providing the same kind of information [2], or in the structural analysis of a Web site. In this paper we propose a novel methodology for Clustering XML documents, focusing on the notion of XML Cluster Representative, i.e., a prototype XML document subsuming the most relevant features of the set of XML documents within the Cluster. In particular, we devise a technique to compute a Representative of a set of XML documents, which is capable of capturing all the structural specificities within the represented documents. To this purpose, the notion of structural matching between the trees associated to two XML documents is exploited. Structural matchings allow to both identify the structural similarities between two XML documents and to build a Representative around these similarities. We also investigate the exploitation of merging and pruning strategies for refining XML document trees into effective Cluster Representatives.
Zhang Jianpen - One of the best experts on this subject based on the ideXlab platform.
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Distributed data stream Clustering algorithm based on affinity propagation
Journal of Computer Applications, 2013Co-Authors: Zhang JianpenAbstract:As to the low Clustering quality and high communication cost of the existed distributed Clustering algorithm, a distributed data stream Clustering algorithm( DAPDC) which combined the density with the idea of Representative points Clustering was proposed. The concept of the class Cluster Representative point to describe the local distribution of data flows was introduced in the local sites using affinity propagation Clustering, while the global site got the global model by merging the summary data structure that was uploaded from the local site by the improved density Clustering algorithm. The simulation results show that DAPDC can improve the Clustering quality of data streams in distributed environment significantly.Simultaneously, the algorithm can find the Clusters of different shapes and reduce the amount of data transferred significantly by using class Cluster Representative points.