A practical clustering technique for learning in an unsupervised mode, based on a new concept of measure of closeness of samples, has been developed. This new approach is found to be capable of zeroing on the truly inherent clusters of the different unspecified pattern classes with a very high degree of certainty
A nonlinear programming approach to the problem of learning in nonparametric and unsupervised environments is put forth in this study. The approach exploits the concept of optimal partitioning of the given data set as the objective of such nonparametric unsupervised learning. This partitioning or clustering is achieved by the application of the recently developed improved flexible polyhedron method (IFPM) to the associated optimization problem. The optimization problem is defined here in terms of a new, conceptually innovative optimality criterion based on intergroup and intragroup distinct scatters. Details of this new approach, a procedure for deriving an initial partition, and application of the algorithm to two numerical examples are presented.
We contrast the discovered preference hypothesis against the theory of coherent arbitrariness in a split-sample stated choice experiment on flood-risk exposure. A semiparametric local multinomial logit model is developed as an alternative to the Swait and Louviere (1993) test procedure controlling for preference dynamics within and between samples. The proposed model supports the discovered preference hypothesis by means of a decaying starting point bias. The Swait and Louviere (1993) test procedure reaches a different conclusion. It rejects the assumption of stable preferences, but most preference dynamics tend to be smoothed out, causing a more erratic pattern of preference dynamics.