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» On exploiting the power of time in data mining
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ICDM
2010
IEEE
115views Data Mining» more  ICDM 2010»
13 years 5 months ago
Polishing the Right Apple: Anytime Classification Also Benefits Data Streams with Constant Arrival Times
Classification of items taken from data streams requires algorithms that operate in time sensitive and computationally constrained environments. Often, the available time for class...
Jin Shieh, Eamonn J. Keogh
PKDD
2007
Springer
109views Data Mining» more  PKDD 2007»
14 years 1 months ago
Matching Partitions over Time to Reliably Capture Local Clusters in Noisy Domains
Abstract. When seeking for small clusters it is very intricate to distinguish between incidental agglomeration of noisy points and true local patterns. We present the PAMALOC algor...
Frank Höppner, Mirko Böttcher
SDM
2007
SIAM
149views Data Mining» more  SDM 2007»
13 years 9 months ago
WAT: Finding Top-K Discords in Time Series Database
Finding discords in time series database is an important problem in a great variety of applications, such as space shuttle telemetry, mechanical industry, biomedicine, and financ...
Yingyi Bu, Oscar Tat-Wing Leung, Ada Wai-Chee Fu, ...
SIGMOD
2000
ACM
133views Database» more  SIGMOD 2000»
13 years 12 months ago
Data Mining on an OLTP System (Nearly) for Free
This paper proposes a scheme for scheduling disk requests that takes advantage of the ability of high-level functions to operate directly at individual disk drives. We show that s...
Erik Riedel, Christos Faloutsos, Gregory R. Ganger...
KDD
2012
ACM
271views Data Mining» more  KDD 2012»
11 years 10 months ago
GigaTensor: scaling tensor analysis up by 100 times - algorithms and discoveries
Many data are modeled as tensors, or multi dimensional arrays. Examples include the predicates (subject, verb, object) in knowledge bases, hyperlinks and anchor texts in the Web g...
U. Kang, Evangelos E. Papalexakis, Abhay Harpale, ...