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Scalable and robust group discovery on large transactional data
P. Choi, A. Moore, and J.M. Kubica
tech. report CMU-RI-TR-05-60, Robotics Institute, Carnegie Mellon University, December, 2005.

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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.

Associated lab/group: Auton Lab
Associated project: Auton Project

Number of pages: 29

P. Choi, A. Moore, and J.M. Kubica, Scalable and robust group discovery on large transactional data, tech. report CMU-RI-TR-05-60, Robotics Institute, Carnegie Mellon University, December, 2005.

@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",
   institution = "Robotics Institute, Carnegie Mellon University",
   month = "December",
   year = "2005",
   number = "CMU-RI-TR-05-60",
   address = "Pittsburgh, PA"
}