Abstract
The starting point of this work is the definition of local pattern detection given in [10] as the unsupervised detection of local regions with anomalously high data density, which represent real underlying phenomena. We discuss some aspects of this definition and examine the differences between clustering and pattern detection (if any), before we investigate how to utilize clustering algorithms for pattern detection. A modification of an existing clustering algorithm is proposed to identify local patterns that are flagged as being significant according to a statistical test.
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Höppner, F. (2005). Local Pattern Detection and Clustering. In: Morik, K., Boulicaut, JF., Siebes, A. (eds) Local Pattern Detection. Lecture Notes in Computer Science(), vol 3539. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11504245_4
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DOI: https://doi.org/10.1007/11504245_4
Publisher Name: Springer, Berlin, Heidelberg
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