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EDBT
2004
ACM
142views Database» more  EDBT 2004»
16 years 7 months ago
Iterative Incremental Clustering of Time Series
We present a novel anytime version of partitional clustering algorithm, such as k-Means and EM, for time series. The algorithm works by leveraging off the multi-resolution property...
Jessica Lin, Michail Vlachos, Eamonn J. Keogh, Dim...
196
Voted
ICSE
2003
IEEE-ACM
16 years 7 months ago
Beyond the Personal Software Process: Metrics collection and analysis for the differently disciplined
Pedagogies such as the Personal Software Process (PSP) shift metrics definition, collection, and analysis from the organizational level to the individual level. While case study r...
Philip M. Johnson, Hongbing Kou, Joy Agustin, Chri...
EWSN
2008
Springer
16 years 7 months ago
Efficient Clustering for Improving Network Performance in Wireless Sensor Networks
Clustering is an important mechanism in large multi-hop wireless sensor networks for obtaining scalability, reducing energy consumption and achieving better network performance. Mo...
Tal Anker, Danny Bickson, Danny Dolev, Bracha Hod
DCC
2005
IEEE
16 years 7 months ago
The Markov Expert for Finding Episodes in Time Series
We describe a domain-independent, unsupervised algorithm for refined segmentation of time series data into meaningful episodes, focusing on the problem of text segmentation. The V...
Jimming Cheng, Michael Mitzenmacher
182
Voted
SDM
2009
SIAM
144views Data Mining» more  SDM 2009»
16 years 4 months ago
CORE: Nonparametric Clustering of Large Numeric Databases.
Current clustering techniques are able to identify arbitrarily shaped clusters in the presence of noise, but depend on carefully chosen model parameters. The choice of model param...
Andrej Taliun, Arturas Mazeika, Michael H. Bö...
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