Hybrid record linkage model for integrating marine data

Devi Fitrianah, Ito Wasito

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

The data integration from two different marine data sources is conducted with the hybrid record linkage model. The method is to find the same key attribute from each source and to decide whether the status is exact, mathed or unmatched. The Hybrid Record Linkage model is the combination between supervised learning and unsupervised learning. The study used set of 325 weekly composite Sea Surface Temperature and Chlorophyll a images derived from MODIS AQUA sensor and covering 4 years from 2007-2010 converted to ASCII form and Tuna Fishing Data from 2007-2010. The result from the study shows that the combination hybrid model GMM clustering and Naive Bayes Classifier identified more matched status and generate more accurate matched records with the average accuracy 0,950677.

Original languageEnglish
Pages (from-to)926-932
Number of pages7
JournalProcedia Engineering
Volume50
DOIs
Publication statusPublished - 1 Dec 2012
Event2012 International Conference on Advances Science and Contemporary Engineering, ICASCE 2012 - Jakarta, Indonesia
Duration: 24 Oct 201225 Oct 2012

Keywords

  • Hybrid record linkage
  • Supervised learning
  • Unsupervised learning

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