Open AccessA New K-Means Clustering Algorithm for Customer Classification in Precision Marketing
Author(s) -
Xiaoling Du Xinwu Li
Publication year - 2021
Publication title -
converter
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.104
H-Index - 1
ISSN - 0010-8189
DOI - 10.17762/converter.227
Subject(s) - cluster analysis , particle swarm optimization , data mining , computer science , convergence (economics) , k means clustering , canopy clustering algorithm , algorithm , cure data clustering algorithm , correlation clustering , machine learning , artificial intelligence , economics , economic growth
K-means is wildly used in data mining and clustering for its powerful data clustering ability, but its inherent limitations affect its application fields and accuracy. Theoriginal K-means algorithm is improved and applied in customer clustering in precision marketing. Firstly, integrates K-means algorithm with particle swarm optimization according to analyzing the source of the K-means calculation limitations; Secondly, improves the improved algorithm in its operation time, convergence speed, global solution exploration ability successively and redesigns the calculation procedures; Finally applies it in customer classification in precision marketing and the experiment results shows that the new algorithm can increasecustomer clustering effectiveness, validity, accuracy and has satisfactory results in practice.