Apeh, E. T., Gabrys, B. and Schierz, A. C., 2011. Customer profile classification using transactional data. In: Third World Congress on Nature and Biologically Inspired Computing (NaBIC), 19-21 Octpber 2011, Salamanca Spain. IEEE, pp. 37-43.
Full text available as:
|PDF - Accepted Version|
Official URL: http://www.mirlabs.org/nabic11/
Customer profiles are by definition made up of factual and transactional data. It is often the case that due to reasons such as high cost of data acquisition and/or protection, only the transactional data are available for data mining operations. Transactional data, however, tend to be highly sparse and skewed due to a large proportion of customers engaging in very few transactions. This can result in a bias in the prediction accuracy of classifiers built using them towards the larger proportion of customers with fewer transactions. This paper investigates an approach for accurately and confidently grouping and classifying customers in bins on the basis of the number of their transactions. The experiments we conducted on a highly sparse and skewed real-world transactional data show that our proposed approach can be used to identify a critical point at which customer profiles can be more confidently distinguished.
|Item Type:||Book Section|
|Uncontrolled Keywords:||Data mining, data prepocessing, decision support systems, classification algorithms, industry applications|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
Technology > Business, Management and Marketing
Generalities > Computer Science and Informatics
|Group:||Faculty of Science and Technology|
|Deposited By:||Unnamed user with email email@example.com|
|Deposited On:||09 Sep 2011 12:16|
|Last Modified:||10 Sep 2014 15:52|
Document DownloadsMore statistics for this item...
|Repository Staff Only -|
|BU Staff Only -|
|Help Guide -||Editing Your Items in BURO|