New Frontiers for Scan Statistics: Network, Trajectory, and Text Data

Renato M. Assunção (), Roberto C. S. N. P. Souza () and Marcos O. Prates ()
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Renato M. Assunção: Universidade Federal de Minas Gerais, Department of Computer Science
Roberto C. S. N. P. Souza: Universidade Federal de Minas Gerais, Department of Computer Science
Marcos O. Prates: Universidade Federal de Minas Gerais, Department of Statistics

Chapter 16 in Handbook of Scan Statistics, 2024, pp 301-324 from Springer

Abstract: Abstract In this chapter we survey the new theoretical developments and the use of scan statistics in data represented as graphs, trajectories, and text. These types of data are becoming common in the new massive digital data world. Large social networks are represented by complex graphs. We have records of the paths of moving objects, such as people who log their travel routes generating GPS trajectories. Large quantities of text are continuously generated by news wire services and social networks. There is a large interest in developing algorithms with strong statistical basis for detecting anomalies in these types of data. We review the use of the scan statistics in these situations. Additionally, we identify three main opportunities and challenges from the big data times for scan statistics: we need to deal with new stochastic data structures; we need much higher computational efficiency than we have now; and we need models that can deal with the variability that appears in the large samples now collected.

Keywords: Scan Statistics; Anomaly Detection; Text Mining; Graph Data Mining; Trajectory Data Mining; Network Data Mining (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-1-4614-8033-4_47

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