Title :
Including auxiliary information in fuzzy clustering
Author :
Kersten, Paul R.
Author_Institution :
Weapons Div., Naval Air Warfare Center, China Lake, CA, USA
Abstract :
Fuzzy clustering is an effective prerequisite exploratory data analysis (EDA) tool to aid in the design of pattern recognition classifiers. Often, signal-to-noise (SNR) information is available with each data sample. The fuzzy C-Means (FCM) and the fuzzy C-Medians (FCMED) clustering algorithms are extended to include this auxiliary information. These extensions are tested using Slash data where the variation in SNR is assumed to be available to the system. Two fuzzy clustering methods are compared with and without the auxiliary information available
Keywords :
data analysis; fuzzy set theory; pattern recognition; statistical analysis; Slash data; auxiliary information; fuzzy C-Means clustering algorithm; fuzzy C-Medians clustering algorithm; fuzzy clustering; pattern recognition classifiers; prerequisite exploratory data analysis tool; signal-to-noise information; Algorithm design and analysis; Clustering algorithms; Clustering methods; Data analysis; Electronic design automation and methodology; Lakes; Signal to noise ratio; System testing; Target recognition; Weapons;
Conference_Titel :
Fuzzy Information Processing Society, 1996. NAFIPS., 1996 Biennial Conference of the North American
Conference_Location :
Berkeley, CA
Print_ISBN :
0-7803-3225-3
DOI :
10.1109/NAFIPS.1996.534735
