Gradual inference rules in approximate reasoning
Information Sciences: an International Journal
A comparative assessment of measures of similarity of fuzzy values
Fuzzy Sets and Systems
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
A comparison of similarity measures of fuzzy values
Fuzzy Sets and Systems
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Finding fuzzy and gradual functional dependencies with SummarySQL
Fuzzy Sets and Systems
Similarity and compatibility in fuzzy set theory: assessment and applications
Similarity and compatibility in fuzzy set theory: assessment and applications
A class of rational cardinality-based similarity measures
Journal of Computational and Applied Mathematics
SAINTETIQ: a fuzzy set-based approach to database summarization
Fuzzy Sets and Systems - Data bases and approximate reasoning
Linguistic summarization of time series using a fuzzy quantifier driven aggregation
Fuzzy Sets and Systems
Linguistic summarization of video for fall detection using voxel person and fuzzy logic
Computer Vision and Image Understanding
On the transitivity of a parametric family of cardinality-based similarity measures
International Journal of Approximate Reasoning
Modeling human activity from voxel person using fuzzy logic
IEEE Transactions on Fuzzy Systems
Meta-theorems on inequalities for scalar fuzzy set cardinalities
Fuzzy Sets and Systems
International Journal of Intelligent Systems
Time series comparison using linguistic fuzzy techniques
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Similarity evaluation of sets of linguistic summaries
International Journal of Intelligent Systems
Hi-index | 0.20 |
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.