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ICDM
2005
IEEE
271views Data Mining» more  ICDM 2005»
14 years 1 months ago
HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence
In this work, we introduce the new problem of finding time series discords. Time series discords are subsequences of a longer time series that are maximally different to all the r...
Eamonn J. Keogh, Jessica Lin, Ada Wai-Chee Fu
JSSPP
2007
Springer
14 years 1 months ago
QBETS: Queue Bounds Estimation from Time Series
Most space-sharing parallel computers presently operated by high-performance computing centers use batch-queuing systems to manage processor allocation. Because these machines are...
Daniel Nurmi, John Brevik, Richard Wolski
SIGMOD
2000
ACM
165views Database» more  SIGMOD 2000»
13 years 11 months ago
Finding Generalized Projected Clusters In High Dimensional Spaces
High dimensional data has always been a challenge for clustering algorithms because of the inherent sparsity of the points. Recent research results indicate that in high dimension...
Charu C. Aggarwal, Philip S. Yu
BMCBI
2008
122views more  BMCBI 2008»
13 years 7 months ago
A practical comparison of two K-Means clustering algorithms
Background: Data clustering is a powerful technique for identifying data with similar characteristics, such as genes with similar expression patterns. However, not all implementat...
Gregory A. Wilkin, Xiuzhen Huang
ICDM
2007
IEEE
196views Data Mining» more  ICDM 2007»
14 years 1 months ago
Diagnosing Similarity of Oscillation Trends in Time Series
Sensor networks have increased the amount and variety of temporal data available, requiring the definition of new techniques for data mining. Related research typically addresses...
Leonardo E. Mariote, Claudia Bauzer Medeiros, Rica...