Multivariate interpolation of arbitrarily spaced data by moving least squares methods
Journal of Computational and Applied Mathematics
Ten lectures on wavelets
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Fuzzy data mining for interesting generalized association rules
Fuzzy Sets and Systems - Theme: Learning and modeling
Point-Based Probabilistic Surfaces to Show Surface Uncertainty
IEEE Transactions on Visualization and Computer Graphics
Mining pure linguistic associations from numerical data
International Journal of Approximate Reasoning
Fuzzy transform in the analysis of data
International Journal of Approximate Reasoning
An image coding/decoding method based on direct and inverse fuzzy transforms
International Journal of Approximate Reasoning
Association-rule knowledge discovery by using a fuzzy mining approach
International Journal of Business Intelligence and Data Mining
A neural network approach to the fuzzy transform
Fuzzy Sets and Systems
Fuzzy transforms: Theory and applications
Fuzzy Sets and Systems
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
Uncertainty and variability in point cloud surface data
SPBG'04 Proceedings of the First Eurographics conference on Point-Based Graphics
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Investigating the relations between the least-squares approximation techniques and the Fuzzy Transform, in this paper we show that the Discrete Fuzzy Transform is invariant with respect to the interpolating and least-squares approximation. Additionally, the Fuzzy Transform is evaluated at any point by simply resampling the continuous approximation underlying the input data. Using numerical linear algebra, we also derive new properties (e.g., stability to noise, additivity with respect to the input data) and characterizations (e.g., radial and dual membership maps) of the Discrete Fuzzy Transform. Finally, we define the geometry- and confidence-driven Discrete Fuzzy Transforms, which take into account the intrinsic geometry and the confidence weights associated to the data.