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.
|Number of pages||7|
|Publication status||Published - 1 Dec 2012|
|Event||2012 International Conference on Advances Science and Contemporary Engineering, ICASCE 2012 - Jakarta, Indonesia|
Duration: 24 Oct 2012 → 25 Oct 2012
- Hybrid record linkage
- Supervised learning
- Unsupervised learning