Clustering time-series data poses problems, which do not exist in traditional clustering in Euclidean space. Specifically, cluster prototype needs to be calculated, where common s...
We present a novel anytime version of partitional clustering algorithm, such as k-Means and EM, for time series. The algorithm works by leveraging off the multi-resolution property...
Jessica Lin, Michail Vlachos, Eamonn J. Keogh, Dim...
Spectral clustering refers to a flexible class of clustering procedures that can produce high-quality clusterings on small data sets but which has limited applicability to large-s...
We present a new Bi-level LSH algorithm to perform approximate k-nearest neighbor search in high dimensional spaces. Our formulation is based on a two-level scheme. In the first ...
In this paper we present new local search algorithms for the Probabilistic Traveling Salesman Problem (PTSP) using sampling and ad-hoc approximation. These algorithms improve both...
Dennis Weyland, Leonora Bianchi, Luca Maria Gambar...