Title :
The Improvement and Application of the Fuzzy K-Prototypes Algorithm
Author_Institution :
Coll. of Comput. & Inf. Technol., Henan Normal Univ., Xinxiang, China
Abstract :
Fuzzy K-prototypes is a very efficient algorithm for processing large scale mixed data set, but the selection of initial clustering center has an important impact on the clustering effect of algorithm. FKP algorithm is improved by using genetic algorithm in this paper. Seeking the initial clustering center for fuzzy K-prototypes algorithm by using genetic algorithm overcomes the shortcoming effectively, which has been applied to customer data segmentation of automobile industry collaborative platform. It is proved that the improved effect is obviously.
Keywords :
automobile industry; customer services; data handling; fuzzy set theory; genetic algorithms; groupware; pattern clustering; automobile industry collaborative platform; customer data segmentation; fuzzy K-prototypes algorithm; genetic algorithm; initial clustering center selection; mixed data set processing; Application software; Biological cells; Clustering algorithms; Convergence; Cost function; Equations; Fuzzy sets; Genetic algorithms; Iterative algorithms; Large-scale systems;
Conference_Titel :
Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4507-3
Electronic_ISBN :
978-1-4244-4507-3
DOI :
10.1109/CISE.2009.5364736
