Mining Hesitation Information by Vague Association Rules
In many online shopping applications, such as Amazon and eBay, traditional Association Rule (AR) mining has limitations as it only deals with the items that are sold but ignores the items that are almost sold (for example, those items that are put into the basket but not checked out). We say that those almost sold items carry hesitation information, since customers are hesitating to buy them. The hesitation information of items is valuable knowledge for the design of good selling strategies. However, there is no conceptual model that is able to capture different statuses of hesitation information. Herein, we apply and extend vague set theory in the context of AR mining. We define the concepts of attractiveness and hesitation of an item, which represent the overall information of a customer’s intent on an item. Based on the two concepts, we propose the notion of Vague Association Rules (VARs). We devise an efficient algorithm to mine the VARs. Our experiments show that our algorithm is efficient and the VARs capture more specific and richer information than do the traditional ARs.
KeywordsAssociation Rule Frequent Itemset Chain Group Online Shopping Minimum Support Threshold
Unable to display preview. Download preview PDF.
- 1.Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: SIGMOD Conference, pp. 207–216 (1993)Google Scholar
- 6.Amazon.com Help, http://www.amazon.com/gp/help/customer/display.html?nodeId=524700
- 9.NLANR, http://www.ircache.net/
- 10.Data Mining Project. The Quest retail transaction data generator (1996), http://www.almaden.ibm.com/software/quest/
- 15.Lu, A., Ng, W.: Handling inconsistency of vague relations with functional dependencies. In: ER (2007)Google Scholar
- 16.Lu, A., Ke, Y., Cheng, J., Ng, W.: Mining vague association rules. In: DASFAA, pp. 891–897 (2007)Google Scholar