Abstract
LUCK allows to use any distance-based clustering algorithm to find linear correlated data. For that a novel distance function is introduced, which takes the distribution of the kNN of points into account and corresponds to the probability of two points being part of the same linear correlation. In this work in progress we tested the distance measure with DBSCAN and k-Means comparing it to the well-known linear correlation clustering algorithms ORCLUS, 4C, COPAC, LMCLUS, and CASH, receiving good results for difficult synthetic data sets containing crossing or non-continuous correlations.
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