Abstract
This study applies K-means method and spectral clustering technique in the customer data analysis of an outfitter in Taipei City, Taiwan. The data set contains transaction records of 551 customers from April 2004 to March 2006. The differences between the two clustering techniques mentioned here are significant. K-means method is more capable of dealing with linear separable input, while spectral clustering technique might have the advantage in non-linear separable input. Thus, it would be of interest to know which clustering technique performs better in a real-world case of evaluating customer value when the type of input space is unknown. By using cluster quality assessment, this study found that spectral clustering technique performs better than K-means method. To summarize the analysis, this study also suggests marketing strategies for each cluster based on the results generated by spectral clustering technique.
| Original language | English |
|---|---|
| Pages (from-to) | 807-815 |
| Number of pages | 9 |
| Journal | Quality and Quantity |
| Volume | 44 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2010 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Social Sciences(all)
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Using K-means method and spectral clustering technique in an outfitter's value analysis. / Chang, En Chi; Huang, Shian-Chang; Wu, Hsin-Hung.
In: Quality and Quantity, Vol. 44, No. 4, 01.01.2010, p. 807-815.Research output: Contribution to journal › Article
TY - JOUR
T1 - Using K-means method and spectral clustering technique in an outfitter's value analysis
AU - Chang, En Chi
AU - Huang, Shian-Chang
AU - Wu, Hsin-Hung
PY - 2010/1/1
Y1 - 2010/1/1
N2 - This study applies K-means method and spectral clustering technique in the customer data analysis of an outfitter in Taipei City, Taiwan. The data set contains transaction records of 551 customers from April 2004 to March 2006. The differences between the two clustering techniques mentioned here are significant. K-means method is more capable of dealing with linear separable input, while spectral clustering technique might have the advantage in non-linear separable input. Thus, it would be of interest to know which clustering technique performs better in a real-world case of evaluating customer value when the type of input space is unknown. By using cluster quality assessment, this study found that spectral clustering technique performs better than K-means method. To summarize the analysis, this study also suggests marketing strategies for each cluster based on the results generated by spectral clustering technique.
AB - This study applies K-means method and spectral clustering technique in the customer data analysis of an outfitter in Taipei City, Taiwan. The data set contains transaction records of 551 customers from April 2004 to March 2006. The differences between the two clustering techniques mentioned here are significant. K-means method is more capable of dealing with linear separable input, while spectral clustering technique might have the advantage in non-linear separable input. Thus, it would be of interest to know which clustering technique performs better in a real-world case of evaluating customer value when the type of input space is unknown. By using cluster quality assessment, this study found that spectral clustering technique performs better than K-means method. To summarize the analysis, this study also suggests marketing strategies for each cluster based on the results generated by spectral clustering technique.
UR - http://www.scopus.com/inward/record.url?scp=77952420807&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77952420807&partnerID=8YFLogxK
U2 - 10.1007/s11135-009-9240-0
DO - 10.1007/s11135-009-9240-0
M3 - Article
AN - SCOPUS:77952420807
VL - 44
SP - 807
EP - 815
JO - Quality and Quantity
JF - Quality and Quantity
SN - 0033-5177
IS - 4
ER -