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SIGMOD
2004
ACM
184views Database» more  SIGMOD 2004»
15 years 10 months ago
Identifying Similarities, Periodicities and Bursts for Online Search Queries
We present several methods for mining knowledge from the query logs of the MSN search engine. Using the query logs, we build a time series for each query word or phrase (e.g., `Th...
Michail Vlachos, Christopher Meek, Zografoula Vage...
77
Voted
EDBT
2004
ACM
142views Database» more  EDBT 2004»
15 years 10 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...
81
Voted
ICDE
2010
IEEE
260views Database» more  ICDE 2010»
15 years 10 months ago
Anonymizing Weighted Social Network Graphs
The increasing popularity of social networks has initiated a fertile research area in information extraction and data mining. Although such analysis can facilitate better understan...
Sudipto Das, Ömer Egecioglu, Amr El Abbadi
93
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SDM
2009
SIAM
161views Data Mining» more  SDM 2009»
15 years 7 months ago
Feature Weighted SVMs Using Receiver Operating Characteristics.
Support Vector Machines (SVMs) are a leading tool in classification and pattern recognition and the kernel function is one of its most important components. This function is used...
Shaoyi Zhang, M. Maruf Hossain, Md. Rafiul Hassan,...
SDM
2009
SIAM
127views Data Mining» more  SDM 2009»
15 years 7 months ago
Event Discovery in Time Series.
The discovery of events in time series can have important implications, such as identifying microlensing events in astronomical surveys, or changes in a patient’s electrocardiog...
Carla E. Brodley, Dan Preston, Pavlos Protopapas
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