An incremental FP-growth web content mining and its application in preference identification

Authors:
Xiaoshu Hang;James N. K. Liu;Yu Ren;Honghua Dai
Affiliations:
School of Information Technology, Deakin University, Melbourne, Australia;Department of Computing, Hong Kong Polytechnic University, Hong Kong;Beijing Capital Science and Technology Group Co., Ltd, Beijing, China;School of Information Technology, Deakin University, Melbourne, Australia
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Year:
2005

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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.