Title :
Fuzzy C-Means Clustering Algorithm Based on Incomplete Data
Author :
Jia, Zhiping ; Yu, Zhiqiang ; Zhang, Chenghui
Author_Institution :
Dept. of Comput. Sci. & Technol., Univ. of Shandong, Jinan
Abstract :
In order to solve the problem that the traditional fuzzy c-means(FCM) clustering algorithm can not directly act on incomplete data, a modified algorithm IDFCM(Incomplete Data FCM) based on the FCM algorithm is proposed. The IDFCM algorithm takes the percentage of incomplete data in datasets and its effect on clustering analysis into consideration. Finally, the experimental clustering results of IRIS data and mobile distributed inspected data of the ocean are given, which can clearly prove that the IDFCM algorithm is very efficient for clustering incomplete data.
Keywords :
fuzzy set theory; pattern clustering; IRIS data; fuzzy c-means clustering algorithm; incomplete data; mobile distributed data; Algorithm design and analysis; Clustering algorithms; Computer science; Data engineering; Fuzzy control; Fuzzy sets; Iris; Oceans; Partitioning algorithms; Prototypes; FCM algorithm; IDFCM algorithm; fuzzy clustering; incomplete datum;
Conference_Titel :
Information Acquisition, 2006 IEEE International Conference on
Conference_Location :
Shandong
Print_ISBN :
1-4244-0528-9
Electronic_ISBN :
1-4244-0529-7
DOI :
10.1109/ICIA.2006.305793