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KDD
2003
ACM
156views Data Mining» more  KDD 2003»
14 years 7 months ago
Mining distance-based outliers in near linear time with randomization and a simple pruning rule
Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal o...
Stephen D. Bay, Mark Schwabacher
IPPS
2005
IEEE
14 years 1 months ago
A Parallel Algorithm for Correlating Event Streams
This paper describes a parallel algorithm for correlating or “fusing” streams of data from sensors and other sources of information. The algorithm is useful for applications w...
Daniel M. Zimmerman, K. Mani Chandy
KDD
2007
ACM
141views Data Mining» more  KDD 2007»
14 years 7 months ago
Detecting anomalous records in categorical datasets
We consider the problem of detecting anomalies in high arity categorical datasets. In most applications, anomalies are defined as data points that are 'abnormal'. Quite ...
Kaustav Das, Jeff G. Schneider
IPPS
2000
IEEE
13 years 12 months ago
ACDS: Adapting Computational Data Streams for High Performance
Data-intensive, interactive applications are an important class of metacomputing (Grid) applications. They are characterized by large, time-varying data flows between data provid...
Carsten Isert, Karsten Schwan
ECML
2007
Springer
14 years 1 months ago
Learning an Outlier-Robust Kalman Filter
We introduce a modified Kalman filter that performs robust, real-time outlier detection, without the need for manual parameter tuning by the user. Systems that rely on high quali...
Jo-Anne Ting, Evangelos Theodorou, Stefan Schaal