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» Anomaly detection by finding feature distribution outliers
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KDD
2001
ACM
163views Data Mining» more  KDD 2001»
14 years 7 months ago
The "DGX" distribution for mining massive, skewed data
Skewed distributions appear very often in practice. Unfortunately, the traditional Zipf distribution often fails to model them well. In this paper, we propose a new probability di...
Zhiqiang Bi, Christos Faloutsos, Flip Korn
ICDE
2005
IEEE
161views Database» more  ICDE 2005»
14 years 8 months ago
Network-Based Problem Detection for Distributed Systems
We introduce a network-based problem detection framework for distributed systems, which includes a data-mining method for discovering dynamic dependencies among distributed servic...
Hisashi Kashima, Tadashi Tsumura, Tsuyoshi Id&eacu...
SDM
2008
SIAM
97views Data Mining» more  SDM 2008»
13 years 9 months ago
Efficient Distribution Mining and Classification
We define and solve the problem of "distribution classification", and, in general, "distribution mining". Given n distributions (i.e., clouds) of multi-dimensi...
Yasushi Sakurai, Rosalynn Chong, Lei Li, Christos ...
KDD
2009
ACM
232views Data Mining» more  KDD 2009»
14 years 8 months ago
Classification of software behaviors for failure detection: a discriminative pattern mining approach
Software is a ubiquitous component of our daily life. We often depend on the correct working of software systems. Due to the difficulty and complexity of software systems, bugs an...
David Lo, Hong Cheng, Jiawei Han, Siau-Cheng Khoo,...
MICCAI
2005
Springer
14 years 8 months ago
Support Vector Clustering for Brain Activation Detection
In this paper, we propose a new approach to detect activated time series in functional MRI using support vector clustering (SVC). We extract Fourier coefficients as the features of...
Defeng Wang, Lin Shi, Daniel S. Yeung, Pheng-Ann H...