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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Höppner, F. (2005). Local pattern detection and clustering are there substantive differences? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3539 LNAI, pp. 53–70). Springer Verlag. https://doi.org/10.1007/11504245_4
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