TY - GEN
T1 - Evolutionary clustering algorithm with knowledge-based evaluation for fuzzy cluster analysis of gene expression profiles
AU - Park, Han Saem
AU - Cho, Sung Bae
PY - 2005
Y1 - 2005
N2 - Clustering method, which groups thousands of genes by their similarities of expression levels, has been used for identifying unknown functions of genes. Fuzzy clustering method that is one category of clustering assigns one sample to multiple groups according to their membership degrees. It is more appropriate than hard clustering algorithms for analyzing gene expression profiles since single gene might involve multiple genetic functions. However, general clustering methods have problems that they are sensitive to initialization and can be trapped into local optima. To solve the problems, we propose an evolutionary fuzzy clustering algorithm with knowledge-based evaluation. It uses a genetic algorithm for clustering and prior knowledge of data for evaluation. Yeast cell-cycle dataset has been used for experiments to show the usefulness of the proposed method.
AB - Clustering method, which groups thousands of genes by their similarities of expression levels, has been used for identifying unknown functions of genes. Fuzzy clustering method that is one category of clustering assigns one sample to multiple groups according to their membership degrees. It is more appropriate than hard clustering algorithms for analyzing gene expression profiles since single gene might involve multiple genetic functions. However, general clustering methods have problems that they are sensitive to initialization and can be trapped into local optima. To solve the problems, we propose an evolutionary fuzzy clustering algorithm with knowledge-based evaluation. It uses a genetic algorithm for clustering and prior knowledge of data for evaluation. Yeast cell-cycle dataset has been used for experiments to show the usefulness of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=33646733868&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33646733868&partnerID=8YFLogxK
U2 - 10.1007/11590316_102
DO - 10.1007/11590316_102
M3 - Conference contribution
AN - SCOPUS:33646733868
SN - 3540305068
SN - 9783540305064
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 640
EP - 644
BT - Pattern Recognition and Machine Intelligence - First International Conference, PReMI 2005, Proceedings
PB - Springer Verlag
T2 - 1st International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005
Y2 - 20 December 2005 through 22 December 2005
ER -