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KDD
2006
ACM
145views Data Mining» more  KDD 2006»
16 years 4 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»
16 years 4 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»
16 years 4 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»
16 years 4 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»
16 years 4 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, ...