| Paper Details: | Downloads: 281 |
| Serial Number: | P1111141872
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| Title: | Classification of Vertically Distributed Data using Directed Acyclic Graph
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| Authors: | Ahmed M. Khedr and Ibrahim Atia
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| Abstract: | Most developed learning algorithms are designed for environments in which all the relevant data is stored at single computer site. With increasingly data volume and appearing of internet and high speed networks, the relevant dataset can be stored in a geographically distributed databases that are connected by communication networks.
These databases cannot be moved to other network sites due to communication costs, security, size, privacy, or data-ownership considerations. In this paper, using directed acyclic graph (DAG), we propose a new k-nearest neighbors classifier k-NN classifier) algorithm for situational
input stored in geographically distributed databases.
We show that it is possible to perform global computation in a reasonably secure manner for vertically partitioned databases. The computation is completed by only exchanging a few local summaries among the databases. An empirical validation of our results is also presented in the paper.
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| Keywords: | Data Privacy, Directed acyclic graph, Nearest
Neighbor Classifier, Vertically distributed Databases.
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| Journal/Conference: | ICGST Conference on Computer Science and Engineering, CSE-11
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| Submission Date: | 10/13/2011 12:00:00 AM
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| Review Date: | 12/4/2011 6:43:15 PM
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| Publishing Date: | 12/19/2011 12:00:00 AM
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| Article Downloads: | 281
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