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KDD
2006
ACM
145views Data Mining» more  KDD 2006»
14 years 10 months ago
Deriving quantitative models for correlation clusters
Correlation clustering aims at grouping the data set into correlation clusters such that the objects in the same cluster exhibit a certain density and are all associated to a comm...
Arthur Zimek, Christian Böhm, Elke Achtert, H...
KDD
2006
ACM
222views Data Mining» more  KDD 2006»
14 years 10 months ago
A component-based framework for knowledge discovery in bioinformatics
Motivation: In the field of bioinformatics there is an emerging need to integrate all knowledge discovery steps into a standardized modular framework. Indeed, component-based deve...
Julien Etienne, Bernd Wachmann, Lei Zhang
KDD
2006
ACM
180views Data Mining» more  KDD 2006»
14 years 10 months ago
Learning the unified kernel machines for classification
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang
KDD
2006
ACM
156views Data Mining» more  KDD 2006»
14 years 10 months ago
Unsupervised learning on k-partite graphs
Various data mining applications involve data objects of multiple types that are related to each other, which can be naturally formulated as a k-partite graph. However, the resear...
Bo Long, Xiaoyun Wu, Zhongfei (Mark) Zhang, Philip...
KDD
2005
ACM
160views Data Mining» more  KDD 2005»
14 years 10 months ago
Consistent bipartite graph co-partitioning for star-structured high-order heterogeneous data co-clustering
Heterogeneous data co-clustering has attracted more and more attention in recent years due to its high impact on various applications. While the co-clustering algorithms for two t...
Bin Gao, Tie-Yan Liu, Xin Zheng, QianSheng Cheng, ...