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Translation and Rotation Invariant Mining of Frequent Trajectories: Application to Protein
 

Summary: Translation and Rotation Invariant Mining of
Frequent Trajectories: Application to Protein
Unfolding Pathways
Alexander Andreopoulos1
, Bill Andreopoulos1,2
, Aijun An1
,
and Xiaogang Wang1
1
York University, Dept. of Computer Science, Toronto Ontario, M3J 1P3, Canada
2
Biotechnological Centre, TU Dresden, Germany
{alekos,billa,aan}@cs.yorku.ca, stevenw@mathstat.yorku.ca
Abstract. We present a framework for mining frequent trajectories,
which are translated and/or rotated with respect to one another. We
then discuss a multiresolution methodology, based on the wavelet trans-
formation, for speeding up the discovery of frequent trajectories. We
present experimental results using noisy protein unfolding trajectories
and synthetic datasets. Our results demonstrate the effectiveness of the
proposed approaches for finding frequent trajectories. A multiresolution

  

Source: An, Aijun - Department of Computer Science, York University (Toronto)

 

Collections: Computer Technologies and Information Sciences