Abstract
Segmenting customers based on transaction data contributes to better understanding and characterizing customers, and has drawn a great deal of attention in literature of various fields. Data mining literature has provided various clustering algorithms for customer segmentation, and distance measure plays an important role in many approaches. However, most distance measures are based on co-occurrence of items, and pay few attention to the sales volume or quantities of items in transactions. In this paper, the probability density of items is employed to gather the description information of transactions and calculate the distance between transactions. Based on distinguishing the difference between similarity measures for transactions and customers, set distance is employed to evaluate the similarity between customers. The whole process is introduced as a framework to reach the target of segmenting customers.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2012 IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012 |
| Pages | 273-278 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | 1st IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012 - Fukuoka, Japan Duration: Sep 20 2012 → Sep 22 2012 |
Other
| Other | 1st IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012 |
|---|---|
| Country | Japan |
| City | Fukuoka |
| Period | 9/20/12 → 9/22/12 |
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All Science Journal Classification (ASJC) codes
- Information Systems
Cite this
A framework for segmenting customers based on probability density of transaction data. / Lu, Ke; Furukawa, Tetsuya.
Proceedings of the 2012 IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012. 2012. p. 273-278 6337202.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A framework for segmenting customers based on probability density of transaction data
AU - Lu, Ke
AU - Furukawa, Tetsuya
PY - 2012
Y1 - 2012
N2 - Segmenting customers based on transaction data contributes to better understanding and characterizing customers, and has drawn a great deal of attention in literature of various fields. Data mining literature has provided various clustering algorithms for customer segmentation, and distance measure plays an important role in many approaches. However, most distance measures are based on co-occurrence of items, and pay few attention to the sales volume or quantities of items in transactions. In this paper, the probability density of items is employed to gather the description information of transactions and calculate the distance between transactions. Based on distinguishing the difference between similarity measures for transactions and customers, set distance is employed to evaluate the similarity between customers. The whole process is introduced as a framework to reach the target of segmenting customers.
AB - Segmenting customers based on transaction data contributes to better understanding and characterizing customers, and has drawn a great deal of attention in literature of various fields. Data mining literature has provided various clustering algorithms for customer segmentation, and distance measure plays an important role in many approaches. However, most distance measures are based on co-occurrence of items, and pay few attention to the sales volume or quantities of items in transactions. In this paper, the probability density of items is employed to gather the description information of transactions and calculate the distance between transactions. Based on distinguishing the difference between similarity measures for transactions and customers, set distance is employed to evaluate the similarity between customers. The whole process is introduced as a framework to reach the target of segmenting customers.
UR - http://www.scopus.com/inward/record.url?scp=84870831310&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84870831310&partnerID=8YFLogxK
U2 - 10.1109/IIAI-AAI.2012.62
DO - 10.1109/IIAI-AAI.2012.62
M3 - Conference contribution
SN - 9780769548265
SP - 273
EP - 278
BT - Proceedings of the 2012 IIAI International Conference on Advanced Applied Informatics, IIAIAAI 2012
ER -