The present state-run of the fine art in the data linkage is to match the entities from the different data sources which do not contain the common identifier. In that here one-tomany data linkage is considered to obtain the decision process based on the clustering tree. In prior work, there is no one-tomany data linkage tasks instead the issue addressed are to link among the same type of the entities. In this paper, two new splitting criterion are introduced to enhance the performance of the linkage process for the best split at each node during the decision tree construction process and securing the linked data from the unauthorized usage. Pruning techniques are implemented to remove the anomalies of the clustering tree.

 

Published In : IJCSN Journal Volume 3, Issue 6

Date of Publication : December 2014

Pages : 478 - 482

Figures : 02

Tables : --

Publication Link : An Improved Data Linkage Technique Based on Clustering Tree for Decision Making

 

 

 

Sathya. T : ME Student, Department of Computer Science and Enginerring KSR College of Engineering, Anna University, Namakkal, Tamilnadu 637215, India

Nithya. K : Assistant Professor, Department of Computer Science and Enginerring KSR College of Engineering, Anna University, Namakkal, Tamilnadu 637215, India

 

 

 

 

 

 

 

Data linkage

clustering tree

splitting criteria

pruning

In this paper, we are proposing the novel method to link the data which does not have the common entity and also based on the clustering tree. Based on this we can match the two different cluster of data from the different dataset. That was the main challenge in the data linkage here we are applying the one to many data linkage technique with the one class clustering tree in particularly database misuse domain. That was takes place by the decision tree technique. Here each node will be considered as the cluster of nodes and the whole data on the different dataset will be matched as the result. Here we attain the improved efficiency of the data linkage process.

 

 

 

 

 

 

 

 

 

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