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» Identifying Clusters from Positive Data
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BMCBI
2008
122views more  BMCBI 2008»
15 years 16 days 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
108
Voted
BMCBI
2005
150views more  BMCBI 2005»
15 years 9 days ago
Discover protein sequence signatures from protein-protein interaction data
Background: The development of high-throughput technologies such as yeast two-hybrid systems and mass spectrometry technologies has made it possible to generate large protein-prot...
Jianwen Fang, Ryan J. Haasl, Yinghua Dong, Gerald ...
132
Voted
ICDE
2012
IEEE
208views Database» more  ICDE 2012»
13 years 2 months ago
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data
—Traditional clustering algorithms identify just a single clustering of the data. Today’s complex data, however, allow multiple interpretations leading to several valid groupin...
Emmanuel Müller, Stephan Günnemann, Ines...
AUSDM
2006
Springer
124views Data Mining» more  AUSDM 2006»
15 years 4 months ago
Analyzing Harmonic Monitoring Data Using Data Mining
Harmonic monitoring has become an important tool for harmonic management in distribution systems. A comprehensive harmonic monitoring program has been designed and implemented on ...
Ali Asheibi, David Stirling, Danny Soetanto
KDD
2007
ACM
152views Data Mining» more  KDD 2007»
16 years 25 days ago
Efficient incremental constrained clustering
Clustering with constraints is an emerging area of data mining research. However, most work assumes that the constraints are given as one large batch. In this paper we explore the...
Ian Davidson, S. S. Ravi, Martin Ester