Coupled Interdependent Attribute Analysis on Mixed Data


  • Can Wang Digital Productivity Flagship, CSIRO
  • Chi-Hung Chi Digital Productivity Flagship, CSIRO
  • Wei Zhou Digital Productivity Flagship, CSIRO
  • Raymond Wong University of New South Wales



Coupling Relationship, Interdependence, Heterogenous Attributes, Mixed Data


In the real-world applications, heterogeneous interdependent attributes that consist of both discrete and numerical variables can be observed ubiquitously. The usual representation of these data sets is an information table, assuming the independence of attributes. However, very often, they are actually interdependent on one another, either explicitly or implicitly. Limited research has been conducted in analyzing such attribute interactions, which causes the analysis results to be more local than global. This paper proposes the coupled heterogeneous attribute analysis to capture the interdependence among mixed data by addressing coupling context and coupling weights in unsupervised learning. Such global couplings integrate the interactions within discrete attributes, within numerical attributes and across them to form the coupled representation for mixed type objects based on dimension conversion and feature selection. This work makes one step forward towards explicitly modeling the interdependence of heterogeneous attributes among mixed data, verified by the applications in data structure analysis, data clustering evaluation, and density comparison. Substantial experiments on 12 UCI data sets show that our approach can effectively capture the global couplings of heterogeneous attributes and outperforms the state-of-the-art methods, supported by statistical analysis.




How to Cite

Wang, C., Chi, C.-H., Zhou, W., & Wong, R. (2015). Coupled Interdependent Attribute Analysis on Mixed Data. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).



Main Track: Machine Learning Applications