Title :
Fuzzy claustering application to marketing data and feature extraction of data
Author :
Tomoko, Ninomiya
Author_Institution :
Tamagawa Univ.
Abstract :
Usually, the population of a large marketing dataset is assumed that a mixture of two or more different groups. In this paper, to classify the observations into the group of some different populations, we propose a fuzzy clustering technique that uses an unobserved variable that influences the observed variables. And, we show an effectiveness of the technique to clarify the data structure and to extract the feature.
Keywords :
data mining; data structures; feature extraction; fuzzy set theory; marketing data processing; pattern clustering; data feature extraction; data structure; fuzzy clustering; marketing data; marketing dataset; Computer performance; Data analysis; Data mining; Data structures; Educational institutions; Feature extraction; Fuzzy sets; Information technology; Internet; Proposals; Fuzzy clustering; Marketing data; observed Variable; unobserved variable;
Conference_Titel :
Automation Congress, 2008. WAC 2008. World
Conference_Location :
Hawaii, HI
Print_ISBN :
978-1-889335-38-4
Electronic_ISBN :
978-1-889335-37-7