TY - GEN
T1 - Multi-layer topology preserving mapping for K-means clustering
AU - Wu, Ying
AU - Doyle, Thomas K.
AU - Fyfe, Colin
PY - 2011
Y1 - 2011
N2 - In this paper, we investigate the multi-layer topology preserving mapping for K-means. We present a Multi-layer Topology Preserving Mapping (MTPM) based on the idea of deep architectures. We demonstrate that the MTPM output can be used to discover the number of clusters for K-means and initialize the prototypes of K-means more reasonably. Also, K-means clusters the data based on the discovered underlying structure of the data by the MTPM. The standard wine data set is used to test our algorithm. We finally analyse a real biological data set with no prior clustering information available.
AB - In this paper, we investigate the multi-layer topology preserving mapping for K-means. We present a Multi-layer Topology Preserving Mapping (MTPM) based on the idea of deep architectures. We demonstrate that the MTPM output can be used to discover the number of clusters for K-means and initialize the prototypes of K-means more reasonably. Also, K-means clusters the data based on the discovered underlying structure of the data by the MTPM. The standard wine data set is used to test our algorithm. We finally analyse a real biological data set with no prior clustering information available.
UR - https://www.scopus.com/pages/publications/80053026929
U2 - 10.1007/978-3-642-23878-9_11
DO - 10.1007/978-3-642-23878-9_11
M3 - Conference proceeding
AN - SCOPUS:80053026929
SN - 9783642238772
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 84
EP - 91
BT - Intelligent Data Engineering and Automated Learning, IDEAL 2011 - 12th International Conference, Proceedings
T2 - 12th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2011
Y2 - 7 September 2011 through 9 September 2011
ER -