Abstract
Mining group differences is useful in many applications, such as medical research, social network analysis and link discovery. The differences between groups can be measured from either statistical or data mining perspective. In this paper, we propose an empirical likelihood (EL) based strategy of building confidence intervals for the mean and distribution differences between two contrasting groups. In our approach we take into account the structure (semi-parametric) of groups, and experimentally evaluate the proposed approach using both simulated and real-world data. The results demonstrate that our approach is effective in building confidence intervals for group differences such as mean and distribution function.
This work is partially supported by Australian large ARC grants (DP0449535 and DP0559536), a China NSF major research Program (60496327), a China NSF grant (60463003), a National Basic Research Program of China (2004CB318103), and a National Science Foundation of China (60033020).
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Huang, Hj., Qin, Y., Zhu, X., Zhang, J., Zhang, S. (2006). Difference Detection Between Two Contrast Sets. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2006. Lecture Notes in Computer Science, vol 4081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11823728_46
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DOI: https://doi.org/10.1007/11823728_46
Publisher Name: Springer, Berlin, Heidelberg
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