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Co-clustering with Augmented Data Matrix

  • Meng-Lun Wu
  • Chia-Hui Chang
  • Rui-Zhe Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6862)

Abstract

Clustering plays an important role in data mining as many applications use it as a preprocessing step for data analysis. Traditional clustering focuses on the grouping of similar objects, while two-way co-clustering can group dyadic data (objects as well as their attributes) simultaneously. Most co-clustering research focuses on single correlation data, but there might be other possible descriptions of dyadic data that could improve co-clustering performance. In this research, we extend ITCC (Information Theoretic Co-Clustering) to the problem of co-clustering with augmented matrix. We proposed CCAM (Co-Clustering with Augmented Data Matrix) to include this augmented data for better co-clustering. We apply CCAM in the analysis of on-line advertising, where both ads and users must be clustered. The key data that connect ads and users are the user-ad link matrix, which identifies the ads that each user has linked; both ads and users also have their feature data, i.e. the augmented data matrix. To evaluate the proposed method, we use two measures: classification accuracy and K-L divergence. The experiment is done using the advertisements and user data from Morgenstern, a financial social website that focuses on the advertisement agency. The experiment results show that CCAM provides better performance than ITCC since it consider the use of augmented data during clustering.

Keywords

Mutual Information Recommendation System Nonnegative Matrix Factorization Augmented Data Dyadic Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Meng-Lun Wu
    • 1
  • Chia-Hui Chang
    • 1
  • Rui-Zhe Liu
    • 1
  1. 1.Dept. of Computer Science and Information EngineeringNational Central UniversityTaoyuanTaiwan

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