Abstract
Pattern discovery is a facet of data mining concerned with the detection of ”small local” structures in large data sets. In high dimensions this is typically difficult because of the computational work involved in searching over the data space. In this paper we outline a tool called PEAKER which can detect patterns efficiently in high dimensions. We approach the subject through the two aspects of pattern discovery, detection and verification. We demonstrate various ways of using PEAKER as well as its various inherent properties, emphasizing the exploratory nature of the tool.
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References
Hand, D.J., Bolton, R.J.: Pattern discovery and detection: a unified statistical methodology. Journal of Applied Statistics 31, 885–924 (2004)
Hand, D.J., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press, Cambridge (2001)
Sain, S.R., Baggerly, K.A., Scott, D.W.: Cross-validation of multivariate densities. Journal of the American Statistical Association 89, 807–817 (1994)
Bolton, R.J., Hand, D.J., Crowder, M.: Significance tests for unsupervised pattern discovery in large continuous multivariate data sets. Computational Statistics and Data Analysis 46, 57–79 (2004)
Silverman, B.: Using kernel density estimates to investigate multimodality. Journal of the Royal Statistical Association. Series B 43, 97–99 (1981)
Minnotte, M.C.: Nonparametric testing of existing modes. The Annals of Statistics 25, 1646–1660 (1997)
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhang, Z., Hand, D.J. (2005). Detecting Groups of Anomalously Similar Objects in Large Data Sets. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds) Advances in Intelligent Data Analysis VI. IDA 2005. Lecture Notes in Computer Science, vol 3646. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552253_46
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DOI: https://doi.org/10.1007/11552253_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28795-7
Online ISBN: 978-3-540-31926-9
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