Aggregate two-way co-clustering of ads and user analysis for online advertisements

Meng Lun Wu, Chia Hui Chang, Rui Zhe Liu, Teng Kai Fan

研究成果: 書貢獻/報告類型會議論文篇章同行評審

1 引文 斯高帕斯(Scopus)


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. The key data that connect ads and users are contained in the user-ad link matrix, which denotes the ads that a user has linked. We proposed a three-staged clustering that makes use of the three data matrices to enhance clustering performance. In addition, an iterative cross co-clustering 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 agent of advertisements. The result shows that three staged clustering provides better performance than traditional clustering, while iterative co-clustering completes the task more efficiently.

主出版物標題ICS 2010 - International Computer Symposium
出版狀態已出版 - 2010
事件2010 International Computer Symposium, ICS 2010 - Tainan, Taiwan
持續時間: 16 12月 201018 12月 2010


名字ICS 2010 - International Computer Symposium


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