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Carnegie Mellon Robotics Institute

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Abstract

The need for time-critical analysis and understanding of the underlying group structure from transactional data has been growing in domains such as law enforcement and customs. Kubica et al. (2003) proposed k-groups, an algorithm based on probabilistic generative model for discovering underlying groups in data. Even though k-groups is reported to be signficantly faster than its predecessor GDA (Kubica et al., 2002), k-groups is too slow and memory-intensive for large data in practice. This paper presents XGDA, a framework for scalable and robust group discovery. Evaluation of the performances of XGDA and k-groups shows that XGDA can handle extremely large datasets in reasonable time and yields more robust solutions than k-groups.

Notes

Associated Lab(s) / Group(s): Auton Lab

Associated Project(s): Auton Project

Number of pages: 29

Text Reference

Pak Yan Choi, Andrew Moore, and Jeremy Martin Kubica, "Scalable and robust group discovery on large transactional data," tech. report CMU-RI-TR-05-60, Robotics Institute, Carnegie Mellon University, December, 2005

BibTeX Reference

@techreport{Choi_2005_5297,
   author = "Pak Yan Choi and Andrew Moore and Jeremy Martin Kubica",
   title = "Scalable and robust group discovery on large transactional data",
   booktitle = "",
   institution = "Robotics Institute",
   month = "December",
   year = "2005",
   number= "CMU-RI-TR-05-60",
   address= "Pittsburgh, PA",
}

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