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Home / programming language / Online Dotnet Projects / OCCT: A One-Class Clustering Tree for Implementing One-to-Many Data Linkage
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OCCT: A One-Class Clustering Tree for Implementing One-to-Many Data Linkage

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SKU: PROJ2012 Categories: 2013 Projects, 2015 Projects, Datamining Projects, Final Year Projects, Online Dotnet Projects Tags: academic projects, Android project 2013-2014, Android project Abstract, Android project list, btech projects, dotnet project 2013-2014, dotnet project Abstract, dotnet project list, elysium technologies abstract, elysium technologies chennai, elysium technologies coimbatore, elysium technologies company, elysium technologies courses, elysium technologies erode, elysium technologies inpant traning, elysium technologies internship, elysium technologies jobs, elysium technologies madurai, elysium technologies mou, elysium technologies pondychery, elysium technologies projectlist, elysium technologies projects, elysium technologies ramnad, elysium technologies salem, elysium technologies software, elysium technologies tirunelveli, elysium technologies trichy, Final Year Projects, java project list, java projects 2013-2014, java projects Abstract, madurai software company, matlab project 2013-2014, matlab project Abstract, matlab project list, mtech projects, phd research work, Php project 2013-2014, Php project Abstract, Php project list, Power Electronic project 2013-2014, Power Electronic project Abstract, Power Electronic project list, project center Bangalore, project center chennai, project center coimbatore, project center Erode, project center Hyderabad, project center Kollam, project center madurai, project center Pandicherry, project center ramnad, project center Salem, project center Tiruneveli, project center trichy, research center, Students Projects, Vlsi project 2013-2014, Vlsi project Abstract, Vlsi project list
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OCCT: A One-Class Clustering Tree for Implementing One-to-Many Data Linkage

Abstract— OCCT: A One-Class Clustering Tree for Implementing One-to-Many Data Linkage. One-to-many data linkage is an essential task in many domains, yet only a handful of prior publications have addressed this issue. Furthermore, while traditionally data linkage is performed among entities of the same type, it is extremely necessary to develop linkage techniques that link between matching entities of different types as well. In this paper, < Final Year Projects > we propose a new one-to-many data linkage method that links between entities of different natures. The proposed method is based on a one-class clustering tree (OCCT) that characterizes the entities that should be linked together. The tree is built such that it is easy to understand and transform into association rules, i.e., the inner nodes consist only of features describing the first set of entities, while the leaves of the tree represent features of their matching entities from the second data set. We propose four splitting criteria and two different pruning methods which can be used for inducing the OCCT. The method was evaluated using data sets from three different domains. The results affirm the effectiveness of the proposed method and show that the OCCT yields better performance in terms of precision and recall (in most cases it is statistically significant) when compared to a C4.5 decision tree-based linkage method.

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