DocumentCode :
3418481
Title :
Mining Consumers´ Most Adaptive Products by Efficient Clustering Algorithm
Author :
Chen, Qingzhang ; Han, Jianghong ; Chu, Yuqing ; Ying, Xiaodong
Author_Institution :
Hefei Univ. of Technol., Hefei
fYear :
2006
fDate :
Nov. 29 2006-Dec. 1 2006
Firstpage :
195
Lastpage :
199
Abstract :
Use clustering methods to discover the individual consumer´s most adaptive products, which can support to make better decisions of marketing service. First, oriented from the consumer´s transactional data that we will mine and targeted by finding some consumer´s most adaptive products, we present a simple and efficient cluster algorithm to put the most similar data into the same group. Then we can find the mined consumer´s most adaptive products from the cluster. Moreover, we propose a Boolean algorithm to improve the performance of the previous.
Keywords :
Boolean functions; customer services; data mining; marketing data processing; pattern clustering; Boolean algorithm; adaptive products; efficient clustering algorithm; marketing service decisions; Association rules; Clustering algorithms; Clustering methods; Credit cards; Data analysis; Data mining; Euclidean distance; Information technology; Partitioning algorithms; Technology forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Reality and Telexistence--Workshops, 2006. ICAT '06. 16th International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
0-7695-2754-X
Type :
conf
DOI :
10.1109/ICAT.2006.84
Filename :
4089238
Link To Document :
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