Although k-means clustering is often applied to time series clustering, the underlying Euclidean distance measure is very restrictive in comparison to the human perception of time ...
We introduce a new domain-independent framework for formulating and efficiently evaluating similarity queries over historical data, where given a history as a sequence of timestam...
A great need exists in many fields of eye-tracking research for a robust and general method for scanpath comparisons. Current measures either quantize scanpaths in space (string e...
Halszka Jarodzka, Kenneth Holmqvist, Marcus Nystr&...
The segmentation of time-series is a constrained clustering problem: the data points should be grouped by their similarity, but with the constraint that all points in a cluster mus...
Abstract--After the seminal work by Taqqu et al. relating selfsimilarity to heavy-tailed distributions, a number of research articles verified that aggregated Internet traffic time...