Improved K-Means Algorithm and Application in Customer Segmentation

Nowadays, clustering algorithms are widely used in the commercial field, such as customer analysis, and this application has achieved good effect. K-means algorithm is by far the most commonly used method for clustering. Although, the time consumption is fairly high when faced with lager-scale data....

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Bibliographic Details
Main Authors: Xiaoping Qin, Shijue Zheng, Ying Huang, Guangsheng Deng
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Application software
clustering
Clustering algorithms
Computer science
customer segmentation
Data mining
Frequency
Information management
K-Means algorithm
Large-scale systems
Resource management
time consumption
triangle inequality
Wearable computers
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Summary:Nowadays, clustering algorithms are widely used in the commercial field, such as customer analysis, and this application has achieved good effect. K-means algorithm is by far the most commonly used method for clustering. Although, the time consumption is fairly high when faced with lager-scale data. In this paper, we improved the K-means algorithm. Our improvement is based on the triangle inequality theorem. We use the improved algorithm to carry out a case study in the customer classification. The experimental results show that the improved method indeed lead to lower time consumption, and therefore more effective for large-scale dataset.
DOI:10.1109/APWCS.2010.63