Autoencoder-based Low-Rank Spectral Ensemble Clustering of Biological Data

This work presents a cluster ensemble algorithm using a combination of low-rank co-association matrix decomposition, deep autoencoder transformation, and spectral clustering. The suggested algorithm is studied on Mice Protein Expression dataset and Cardiotocography dataset. Monte-Carlo simulations a...

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Bibliographic Details
Published in2020 Cognitive Sciences, Genomics and Bioinformatics (CSGB) pp. 43 - 46
Main Author Berikov, Vladimir
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2020
Subjects
autoencoder
Bioinformatics
Cardiography
cardiotocography
Clustering algorithms
ensemble clustering
low-rank decomposition
Matrix decomposition
Mice
mice protein expression
Partitioning algorithms
Proteins
spectral clustering
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DOI10.1109/CSGB51356.2020.9214622

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Summary:This work presents a cluster ensemble algorithm using a combination of low-rank co-association matrix decomposition, deep autoencoder transformation, and spectral clustering. The suggested algorithm is studied on Mice Protein Expression dataset and Cardiotocography dataset. Monte-Carlo simulations are used to evaluate the clustering performance. The experiments show that the proposed algorithm significantly outperforms other considered variants of clustering framework with respect to clustering accuracy.
DOI:10.1109/CSGB51356.2020.9214622