Using K-means method and spectral clustering technique in an outfitter's value analysis

Research output: Contribution to journalArticle

14 Citations (Scopus)

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 languageEnglish
Pages (from-to)807-815
Number of pages9
JournalQuality and Quantity
Volume44
Issue number4
DOIs
Publication statusPublished - 2010 Jan 1

Fingerprint

value analysis
Spectral Clustering
K-means
customer
Customers
transaction
Taiwan
data analysis
marketing
Clustering
Quality Assessment
Transactions
Values
Data analysis
Unknown

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Social Sciences(all)

Cite this

@article{b2dcf51edec0455fb3c69d4cfa4372ca,
title = "Using K-means method and spectral clustering technique in an outfitter's value analysis",
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.",
author = "Chang, {En Chi} and Shian-Chang Huang and Hsin-Hung Wu",
year = "2010",
month = "1",
day = "1",
doi = "10.1007/s11135-009-9240-0",
language = "English",
volume = "44",
pages = "807--815",
journal = "Quality and Quantity",
issn = "0033-5177",
publisher = "Springer Netherlands",
number = "4",

}

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 journalArticle

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 -