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Journal of Information Science and Engineering, Vol. 28 No. 1, pp. 83-97 (January 2012)

Aggregate Two-Way Co-Clustering of Ads and User Data for Online Advertisements*

MENG-LUN WU, CHIA-HUI CHANG, RUI-ZHE LIU AND TENG-KAI FAN
Department of Computer Science and Information Engineering
National Central University
Taoyuan, 320 Taiwan

Clustering plays an important role in data mining, as it is used by many applications as a preprocessing step for data analysis. Traditional clustering focuses on grouping similar objects, while two-way co-clustering can group dyadic data (objects as well as their attributes) simultaneously. In this research, we apply two-way co-clustering to the analysis of online advertising where both ads and users need to be clustered. However, in addition to the ad-user link matrix that denotes the ads which a user has linked, we also have two additional matrices, which represent extra information about users and ads. In this paper, we proposed a 3-staged clustering method that makes use of the three data matrices to enhance clustering performance. In addition, an Iterative Cross Co-Clustering (ICCC) algorithm is also proposed for two-way co-clustering. The experiment is performed using the advertisement and user data from Morgenstern, a financial social website that focuses on the agency of advertisements. The result shows that iterative cross co-clustering provides better performance than traditional clustering and completes the task more efficiently.

Keywords: co-clustering, decision tree, KL divergence, dyadic data analysis, clustering evaluation

Full Text () Retrieve PDF document (201201_06.pdf)

Received February 22, 2011; revised August 20, 2011; accepted August 31, 2011.
Communicated by Irwin King.
* This paper has been presented in the International Computer Symposium 2010 (ICS 2010) which was held in Tainan, Taiwan on Dec. 16-18, 2010 and sponsored by IEEE, NSC Taiwan, and MOE Taiwan.