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» Event Discovery in Time Series.
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KDD
2009
ACM
181views Data Mining» more  KDD 2009»
14 years 2 months ago
An exploration of climate data using complex networks
To discover patterns in historical data, climate scientists have applied various clustering methods with the goal of identifying regions that share some common climatological beha...
Karsten Steinhaeuser, Nitesh V. Chawla, Auroop R. ...
SIGKDD
2008
149views more  SIGKDD 2008»
13 years 7 months ago
Knowledge discovery from sensor data (SensorKDD)
Wide-area sensor infrastructures, remote sensors, RFIDs, and wireless sensor networks yield massive volumes of disparate, dynamic, and geographically distributed data. As such sen...
Ranga Raju Vatsavai, Olufemi A. Omitaomu, Joao Gam...
NIPS
1998
13 years 9 months ago
Coding Time-Varying Signals Using Sparse, Shift-Invariant Representations
A common way to represent a time series is to divide it into shortduration blocks, each of which is then represented by a set of basis functions. A limitation of this approach, ho...
Michael S. Lewicki, Terrence J. Sejnowski
ICDE
2003
IEEE
149views Database» more  ICDE 2003»
14 years 9 months ago
Indexing Weighted-Sequences in Large Databases
We present an index structure for managing weightedsequences in large databases. A weighted-sequence is defined as a two-dimensional structure where each element in the sequence i...
Haixun Wang, Chang-Shing Perng, Wei Fan, Sanghyun ...
IPMI
2003
Springer
14 years 8 months ago
Analysis of Event-Related fMRI Data Using Best Clustering Bases
We explore a new paradigm for the analysis of event-related functional magnetic resonance images (fMRI) of brain activity. We regard the fMRI data as a very large set of time serie...
François G. Meyer, Jatuporn Chinrungrueng