Singular vector decomposition based hybrid pattern search: An efficient co-clustering method

Debby D. WANG, Haoran XIE, Fu Lee WANG, Hong YAN

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

With the rapid development of machine-learning and data-mining techniques, biclustering (co-clustering) has become an important and widespread technique in multiple areas such as gene expression analysis, text mining and market segmentation. In this work, we proposed an efficient co-clustering method named SVD-based hybrid pattern search (SHPS). It is a score-function-based method, and specifically both the mean-square-residue and correlation-based scores were tested in our studies. For a data matrix, SHPS first uses SVD layers to approximate it, and then searches the SVD subspaces for hybrid patterns (cliquish or linear) along the row or column direction. Groups along the two directions are combined, and those with a score smaller than a pre-defined threshold will be outputted. After testing our method on multiple types of matrices and comparing it with the traditional Cheng and Church method, SHPS showed a good performance with multiple co-clusters and better scores. Additionally, using more SVD layers may further improve the results. Overall, SHPS can be a good and efficient alternative in future co-clustering-related studies and applications. Copyright © 2016 by the Institute of Electrical and Electronics Engineers.
Original languageEnglish
Title of host publicationProceeding of 2016 International Conference on Machine Learning and Cybernetics
Place of PublicationDanvers, MA
PublisherIEEE
Pages269-274
Volume1
ISBN (Print)9781509003891
DOIs
Publication statusPublished - 2016

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Singular value decomposition
Decomposition
Religious buildings
Gene expression
Data mining
Learning systems
Electronic equipment
Engineers
Testing

Citation

Wang, D. D., Xie, H., Wang, F. L., & Yan, H. (2016). Singular vector decomposition based hybrid pattern search: An efficient co-clustering method. In Proceeding of 2016 International Conference on Machine Learning and Cybernetics (Vol.1, pp. 269-274). Danvers, MA: IEEE.

Keywords

  • Gene expression
  • Algorithm design and analysis
  • Transmission line matrix methods
  • Cybernetics
  • Clustering algorithms
  • Education
  • Text mining