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» Characteristic-Based Clustering for Time Series Data
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SDM
2010
SIAM
156views Data Mining» more  SDM 2010»
13 years 10 months ago
Unsupervised Discovery of Abnormal Activity Occurrences in Multi-dimensional Time Series, with Applications in Wearable Systems
We present a method for unsupervised discovery of abnormal occurrences of activities in multi-dimensional time series data. Unsupervised activity discovery approaches differ from ...
Alireza Vahdatpour, Majid Sarrafzadeh
CVPR
2012
IEEE
11 years 11 months ago
Group action induced distances for averaging and clustering Linear Dynamical Systems with applications to the analysis of dynami
We introduce a framework for defining a distance on the (non-Euclidean) space of Linear Dynamical Systems (LDSs). The proposed distance is induced by the action of the group of o...
Bijan Afsari, Rizwan Chaudhry, Avinash Ravichandra...
ICDM
2006
IEEE
193views Data Mining» more  ICDM 2006»
14 years 2 months ago
Feature Subset Selection on Multivariate Time Series with Extremely Large Spatial Features
Several spatio-temporal data collected in many applications, such as fMRI data in medical applications, can be represented as a Multivariate Time Series (MTS) matrix with m rows (...
Hyunjin Yoon, Cyrus Shahabi
KDD
1998
ACM
141views Data Mining» more  KDD 1998»
14 years 24 days ago
Rule Discovery from Time Series
We consider the problem of nding rules relating patterns in a time series to other patterns in that series, or patterns in one series to patterns in another series. A simple examp...
Gautam Das, King-Ip Lin, Heikki Mannila, Gopal Ren...
ESANN
1998
13 years 10 months ago
Recurrent SOM with local linear models in time series prediction
Recurrent Self-Organizing Map (RSOM) is studied in three di erent time series prediction cases. RSOM is used to cluster the series into local data sets, for which corresponding lo...
Timo Koskela, Markus Varsta, Jukka Heikkonen, Kimm...