TY - GEN
T1 - A parameter free approach for clustering analysis
AU - Huang, Haiqiao
AU - Mok, Pik Yin
AU - Kwok, Yi Lin
AU - Au, Sau Chuen Joe
PY - 2009/10/1
Y1 - 2009/10/1
N2 - In the paper, we propose a novel parameter free approach for clustering analysis. The approach needs not to make assumptions or define parameters on the cluster number or the results, while the clustered results are visually verified and approved by experimental work. For simplicity, this paper demonstrates the idea using Fuzzy C-Means (FCMs) clustering method, but the proposed open framework allows easy integration with other clustering methods. The method-independent framework generates optimal clustering results and avoids intrinsic biases from individual clustering methods.
AB - In the paper, we propose a novel parameter free approach for clustering analysis. The approach needs not to make assumptions or define parameters on the cluster number or the results, while the clustered results are visually verified and approved by experimental work. For simplicity, this paper demonstrates the idea using Fuzzy C-Means (FCMs) clustering method, but the proposed open framework allows easy integration with other clustering methods. The method-independent framework generates optimal clustering results and avoids intrinsic biases from individual clustering methods.
UR - http://www.scopus.com/inward/record.url?scp=70349453976&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-03767-2_99
DO - 10.1007/978-3-642-03767-2_99
M3 - Conference article published in proceeding or book
SN - 3642037666
SN - 9783642037665
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 816
EP - 823
BT - Computer Analysis of Images and Patterns - 13th International Conference, CAIP 2009, Proceedings
T2 - 13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009
Y2 - 2 September 2009 through 4 September 2009
ER -