TY - JOUR
T1 - A distance metric for a space of linguistic summaries.
AU - Wilbik, Anna
AU - Keller, James M.
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2012
Y1 - 2012
N2 - Producing linguistic summaries of large databases or temporal sequences of measurements is an endeavor that is receiving increasing attention. These summaries can be used in a continuous monitoring situation, like eldercare, where it is important to ascertain if the current summaries represent an abnormal condition. It is therefore necessary to compute the distance between summaries as a basis for such a determination. In this paper, we propose a dissimilarity measure between summaries based on fuzzy protoforms, and prove that this measure is a metric. We take into account not only the linguistic meaning of the summaries, but also two quality evaluations, namely the truth values and the degrees of focus. We present examples of how the distance metric behaves and show that it corresponds with intuition. (C) 2012 Elsevier B.V. All rights reserved.
AB - Producing linguistic summaries of large databases or temporal sequences of measurements is an endeavor that is receiving increasing attention. These summaries can be used in a continuous monitoring situation, like eldercare, where it is important to ascertain if the current summaries represent an abnormal condition. It is therefore necessary to compute the distance between summaries as a basis for such a determination. In this paper, we propose a dissimilarity measure between summaries based on fuzzy protoforms, and prove that this measure is a metric. We take into account not only the linguistic meaning of the summaries, but also two quality evaluations, namely the truth values and the degrees of focus. We present examples of how the distance metric behaves and show that it corresponds with intuition. (C) 2012 Elsevier B.V. All rights reserved.
U2 - 10.1016/J.FSS.2012.03.010
DO - 10.1016/J.FSS.2012.03.010
M3 - Article
SN - 0165-0114
VL - 208
SP - 79
EP - 94
JO - Fuzzy Sets and Systems
JF - Fuzzy Sets and Systems
ER -