Applying Cluster Refinement to Improve Crowd-Based Data Duplicate Detection Approach

Applying Cluster Refinement to Improve Crowd-Based Data Duplicate Detection Approach

In this paper, we present an extension on a hybrid-based deduplication technique in entity reconciliation (ER), by proposing an algorithm that builds clusters upon receiving a pre-specified K number of clusters, and second developing a crowd-based procedure for refining the results of the clusters p...

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Bibliographic Details
Main Authors: Charles Roland Haruna, Mengshu Hou, Rui Xi, Moses Jojo Eghan, Michael Y. Kpiebaareh, lawrence Tandoh, Barbie Eghan-Yartel, Maame G. Asante-Mensah
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Cluster refinement
minimization approach
triangular split and merger operations
entity reconciliation
crowdsourcing
Online Access:https://ieeexplore.ieee.org/document/8730331/

Internet

https://ieeexplore.ieee.org/document/8730331/

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