Title :
Similarity measurement for data with high-dimensional and mixed feature values through fuzzy clustering
Author :
Liu Haitao ; Ru-xiang, Wei ; Guo-ping, Jiang
Author_Institution :
Dept. of Equip. Econ. & Manage., Naval Univ. of Eng., Wuhan, China
Abstract :
For data with high-dimensional and mixed feature values, traditional similarity measurement becomes no longer applicable. In this paper, a new similarity measurement is proposed by designing a high dimension FCM clustering algorithm. Firstly, an initialization of ordinal-numerical mappings is given; secondly, new ordinal-numerical mappings are learned from the iterative high dimension FCM clustering algorithm and the clustering effect becomes optimized at the same time; finally, a new similarity measurement for data with high-dimensional and mixed feature values is proposed with the fuzzy partition matrix. Experimental results show that the similarity measurement improves the precision of estimation.
Keywords :
data handling; fuzzy set theory; matrix algebra; pattern clustering; fuzzy clustering; fuzzy partition matrix; high dimension FCM clustering algorithm; high-dimensional values; mixed feature values; ordinal-numerical mappings; similarity measurement; Clustering algorithms; Educational institutions; Estimation; Euclidean distance; Partitioning algorithms; Software; high dimensionality; nominal feature; ordinal feature; similarity; similarity measurement;
Conference_Titel :
Computer Science and Automation Engineering (CSAE), 2012 IEEE International Conference on
Conference_Location :
Zhangjiajie
Print_ISBN :
978-1-4673-0088-9
DOI :
10.1109/CSAE.2012.6273028