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» Graph-based anomaly detection
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130
Voted
SDM
2011
SIAM
233views Data Mining» more  SDM 2011»
14 years 5 months ago
Distributed Monitoring of the R2 Statistic for Linear Regression
The problem of monitoring a multivariate linear regression model is relevant in studying the evolving relationship between a set of input variables (features) and one or more depe...
Kanishka Bhaduri, Kamalika Das, Chris Giannella
161
Voted
SDM
2011
SIAM
256views Data Mining» more  SDM 2011»
14 years 5 months ago
Temporal Structure Learning for Clustering Massive Data Streams in Real-Time
This paper describes one of the first attempts to model the temporal structure of massive data streams in real-time using data stream clustering. Recently, many data stream clust...
Michael Hahsler, Margaret H. Dunham
115
Voted
ICAPR
2005
Springer
15 years 8 months ago
Weighted Adaptive Neighborhood Hypergraph Partitioning for Image Segmentation
Abstract. The aim of this paper is to present an improvement of a previously published algorithm. The proposed approach is performed in two steps. In the first step, we generate t...
Soufiane Rital, Hocine Cherifi, Serge Miguet
SDM
2008
SIAM
120views Data Mining» more  SDM 2008»
15 years 4 months ago
Spatial Scan Statistics for Graph Clustering
In this paper, we present a measure associated with detection and inference of statistically anomalous clusters of a graph based on the likelihood test of observed and expected ed...
Bei Wang, Jeff M. Phillips, Robert Schreiber, Denn...
EMNLP
2006
15 years 4 months ago
Unsupervised Information Extraction Approach Using Graph Mutual Reinforcement
Information Extraction (IE) is the task of extracting knowledge from unstructured text. We present a novel unsupervised approach for information extraction based on graph mutual r...
Hany Hassan, Ahmed Hassan, Ossama Emam