TY - GEN
T1 - Singular vector decomposition based hybrid pattern search - An efficient co-clustering method
AU - Wang, Debby D.
AU - Xie, Haoran
AU - Wang, Fu Lee
AU - Yan, Hong
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - 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.
AB - 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.
KW - Co-clustering
KW - Pattern search
KW - SVD layer
KW - Singular vector decomposition (SVD)
UR - http://www.scopus.com/inward/record.url?scp=85021056987&partnerID=8YFLogxK
U2 - 10.1109/ICMLC.2016.7860912
DO - 10.1109/ICMLC.2016.7860912
M3 - Conference contribution
AN - SCOPUS:85021056987
T3 - Proceedings - International Conference on Machine Learning and Cybernetics
SP - 269
EP - 274
BT - Proceedings of 2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016
T2 - 2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016
Y2 - 10 July 2016 through 13 July 2016
ER -