Abstract
This paper presents a real application of Web-content mining using an incremental FP-Growth approach. We firstly restructure the semi-structured data retrieved from the web pages of Chinese car market to fit into the local database, and then employ an incremental algorithm to discover the association rules for the identification of car preference. To find more general regularities, a method of attribute-oriented induction is also utilized to find customer’s consumption preferences. Experimental results show some interesting consumption preference patterns that may be beneficial for the government in making policy to encourage and guide car consumption.
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© 2005 Springer-Verlag Berlin Heidelberg
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Hang, X., Liu, J.N.K., Ren, Y., Dai, H. (2005). An Incremental FP-Growth Web Content Mining and Its Application in Preference Identification. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553939_18
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DOI: https://doi.org/10.1007/11553939_18
Publisher Name: Springer, Berlin, Heidelberg
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