One-to-Many Data Linkage By One-Class Clustering Tree

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One-to-Many Data Linkage By One-Class Clustering Tree

Journal Title: International Journal of Research in Computer and Communication Technology - Year 2015, Vol 4, Issue 8

Abstract

Data linkage is the task of identifying different entries that refer to the same entity across different data sources. The goal of the data linkage task is joining data sets that do not share a common identifier. Data linkage is divided into two types: one-to-one and one-to-many. In one-to-one data linkage, the goal is to associate an entity from one data set with a single matching entity in another data set. In one-to-many data linkage, the goal is to associate an entity from the first data set with a group of matching entities from the other data set. We implement a new one-to-many data linkage method that links between entities of different natures. In addition, while data linkage is usually performed among entities of the same type, the data linkage technique can match entities of different types. The data linkage method is based on a one-class clustering tree (OCCT) that characterizes the entities that should be linked together.

Authors and Affiliations

P. Aruna kumari, Dr. Srinivas Prasad

Keywords

Clustering classification data matching decision tree induction

EP ID EP28238
DOI -
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