In this paper we propose a new clustering algorithm which combines the FCM clustering algorithm with the supervised learning normal mixture model; we call the algorithm as the FCM...
Our main result is a reduction from worst-case lattice problems such as GAPSVP and SIVP to a certain learning problem. This learning problem is a natural extension of the `learnin...
In this paper we propose a new partial closure-based constrained clustering algorithm. We introduce closures into the partial constrained clustering and we propose a new measureme...
We initiate the study of multi-source extractors in the quantum world. In this setting, our goal is to extract random bits from two independent weak random sources, on which two q...
Estimating the optimal number of clusters for a dataset is one of the most essential issues in cluster analysis. An improper pre-selection for the number of clusters might easily ...