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- W2107377310 abstract "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." @default.
- W2107377310 created "2016-06-24" @default.
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- W2107377310 date "2010-12-01" @default.
- W2107377310 modified "2023-10-16" @default.
- W2107377310 title "Aggregate two-way co-clustering of ads and user analysis for online advertisements" @default.
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- W2107377310 doi "https://doi.org/10.1109/compsym.2010.5685445" @default.
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