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» A Method for Dynamic Clustering of Data
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ICDM
2009
IEEE
132views Data Mining» more  ICDM 2009»
16 years 1 months ago
Bayesian Overlapping Subspace Clustering
Given a data matrix, the problem of finding dense/uniform sub-blocks in the matrix is becoming important in several applications. The problem is inherently combinatorial since th...
Qiang Fu, Arindam Banerjee
EVOW
2006
Springer
15 years 10 months ago
Mining Structural Databases: An Evolutionary Multi-Objetive Conceptual Clustering Methodology
Abstract. The increased availability of biological databases containing representations of complex objects permits access to vast amounts of data. In spite of the recent renewed in...
Rocío Romero-Záliz, Cristina Rubio-E...
PODS
2005
ACM
115views Database» more  PODS 2005»
16 years 6 months ago
A divide-and-merge methodology for clustering
We present a divide-and-merge methodology for clustering a set of objects that combines a top-down "divide" phase with a bottom-up "merge" phase. In contrast, ...
David Cheng, Santosh Vempala, Ravi Kannan, Grant W...
SDM
2009
SIAM
223views Data Mining» more  SDM 2009»
16 years 3 months ago
Context Aware Trace Clustering: Towards Improving Process Mining Results.
Process Mining refers to the extraction of process models from event logs. Real-life processes tend to be less structured and more flexible. Traditional process mining algorithms...
R. P. Jagadeesh Chandra Bose, Wil M. P. van der Aa...
ICML
2007
IEEE
16 years 7 months ago
Maximum margin clustering made practical
Maximum margin clustering (MMC) is a recent large margin unsupervised learning approach that has often outperformed conventional clustering methods. Computationally, it involves n...
Kai Zhang, Ivor W. Tsang, James T. Kwok