caa a22 4500 605478031 CHVBK 20210128100403.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s10994-013-5334-y doi (NATIONALLICENCE)springer-10.1007/s10994-013-5334-y The blind men and the elephant: on meeting the problem of multiple truths in data from clustering and pattern mining perspectives [Elektronische Daten] [Arthur Zimek, Jilles Vreeken] In this position paper, we discuss how different branches of research on clustering and pattern mining, while rather different at first glance, in fact have a lot in common and can learn a lot from each other's solutions and approaches. We give brief introductions to the fundamental problems of different sub-fields of clustering, especially focusing on subspace clustering, ensemble clustering, alternative (as a variant of constraint) clustering, and multiview clustering (as a variant of alternative clustering). Second, we relate a representative of these areas, subspace clustering, to pattern mining. We show that, while these areas use different vocabularies and intuitions, they share common roots and they are exposed to essentially the same fundamental problems; in particular, we detail how certain problems currently faced by the one field, have been solved by the other field, and vice versa. The purpose of our survey is to take first steps towards bridging the linguistic gap between different (sub-) communities and to make researchers from different fields aware of the existence of similar problems (and, partly, of similar solutions or of solutions that could be transferred) in the literature on the other research topic. The Author(s), 2013 Subspace clustering nationallicence Pattern mining nationallicence Ensemble clustering nationallicence Alternative clustering nationallicence Constraint clustering nationallicence Multiview clustering nationallicence Zimek Arthur Department of Computing Science, University of Alberta, Edmonton, AB, Canada aut Vreeken Jilles Advanced Database Research and Modelling, Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium aut Machine Learning Springer US; http://www.springer-ny.com 98/1-2(2015-01-01), 121-155 0885-6125 98:1-2<121 2015 98 10994 https://doi.org/10.1007/s10994-013-5334-y text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-013-5334-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zimek Arthur Department of Computing Science, University of Alberta, Edmonton, AB, Canada aut NATIONALLICENCE 700 1- Vreeken Jilles Advanced Database Research and Modelling, Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 98/1-2(2015-01-01), 121-155 0885-6125 98:1-2<121 2015 98 10994