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

作者：Wu Meng Lun^*; Chang Chia Hui; Liu Rui Zhe; Fan Teng Kai

来源：Journal of Information Science and Engineering, 2012, 28(1): 83-97.

• co-clustering
• decision tree
• KL divergence
• dyadic data analysis
• clustering evaluation

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.

• 出版日期2012-1
• 单位中央民族大学