Performance improvement in Resampling Based Clustering
Author(s) -
Okta Qomaruddin Aziz
Publication year - 2020
Publication title -
matics jurnal ilmu komputer dan teknologi informasi (journal of computer science and information technology)
Language(s) - English
Resource type - Journals
eISSN - 2477-2550
pISSN - 1978-161X
DOI - 10.18860/mat.v12i1.8918
Subject(s) - normalization (sociology) , cluster analysis , resampling , computer science , data mining , artificial intelligence , pattern recognition (psychology) , mathematics , sociology , anthropology
Clustering is one of powerful technique to find a biological mechanism in gene expression. This technique identify a gene that has same expression. Using bootstrap method we can improve the quality of microarray, thus resampling based clustering (RC) is consider one of the improvement. RC use K-means clustering to determine initial parameter and need thousands of iteration to converge. Performance improvement can be done at preprocess, such as normalization and changing the initial parameter. Normalization can remove or lower the bias in microarray. The result show that normalization can improve the accuracy of RC. In addition, for parameter K, a lower value will lower the accuracy of this RC.
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