5,548 research outputs found
Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare
For the last years, time-series mining has become a challenging issue for
researchers. An important application lies in most monitoring purposes, which
require analyzing large sets of time-series for learning usual patterns. Any
deviation from this learned profile is then considered as an unexpected
situation. Moreover, complex applications may involve the temporal study of
several heterogeneous parameters. In that paper, we propose a method for mining
heterogeneous multivariate time-series for learning meaningful patterns. The
proposed approach allows for mixed time-series -- containing both pattern and
non-pattern data -- such as for imprecise matches, outliers, stretching and
global translating of patterns instances in time. We present the early results
of our approach in the context of monitoring the health status of a person at
home. The purpose is to build a behavioral profile of a person by analyzing the
time variations of several quantitative or qualitative parameters recorded
through a provision of sensors installed in the home
Rough Sets Clustering and Markov model for Web Access Prediction
Discovering user access patterns from web access log is increasing the importance of information to build up adaptive web server according to the individual user’s behavior. The variety of user behaviors on accessing information also grows, which has a great impact on the network utilization. In this paper, we present a rough set clustering to cluster web transactions from web access logs and using Markov model for next access prediction. Using this approach, users can effectively mine web log records to discover and predict access patterns. We perform experiments using real web trace logs collected from www.dusit.ac.th servers. In order to improve its prediction ration, the model includes a rough sets scheme in which search similarity measure to compute the similarity between two sequences using upper approximation
Kinetic distance and kinetic maps from molecular dynamics simulation
Characterizing macromolecular kinetics from molecular dynamics (MD)
simulations requires a distance metric that can distinguish
slowly-interconverting states. Here we build upon diffusion map theory and
define a kinetic distance for irreducible Markov processes that quantifies how
slowly molecular conformations interconvert. The kinetic distance can be
computed given a model that approximates the eigenvalues and eigenvectors
(reaction coordinates) of the MD Markov operator. Here we employ the
time-lagged independent component analysis (TICA). The TICA components can be
scaled to provide a kinetic map in which the Euclidean distance corresponds to
the kinetic distance. As a result, the question of how many TICA dimensions
should be kept in a dimensionality reduction approach becomes obsolete, and one
parameter less needs to be specified in the kinetic model construction. We
demonstrate the approach using TICA and Markov state model (MSM) analyses for
illustrative models, protein conformation dynamics in bovine pancreatic trypsin
inhibitor and protein-inhibitor association in trypsin and benzamidine
- …