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KDD
2007
ACM
168views Data Mining» more  KDD 2007»
16 years 7 months ago
A probabilistic framework for relational clustering
Relational clustering has attracted more and more attention due to its phenomenal impact in various important applications which involve multi-type interrelated data objects, such...
Bo Long, Zhongfei (Mark) Zhang, Philip S. Yu
EDBT
2008
ACM
167views Database» more  EDBT 2008»
16 years 6 months ago
HISSCLU: a hierarchical density-based method for semi-supervised clustering
In situations where class labels are known for a part of the objects, a cluster analysis respecting this information, i.e. semi-supervised clustering, can give insight into the cl...
Christian Böhm, Claudia Plant
SDM
2009
SIAM
225views Data Mining» more  SDM 2009»
16 years 3 months ago
Integrated KL (K-means - Laplacian) Clustering: A New Clustering Approach by Combining Attribute Data and Pairwise Relations.
Most datasets in real applications come in from multiple sources. As a result, we often have attributes information about data objects and various pairwise relations (similarity) ...
Fei Wang, Chris H. Q. Ding, Tao Li
CIKM
2004
Springer
15 years 12 months ago
Soft clustering criterion functions for partitional document clustering: a summary of results
Recently published studies have shown that partitional clustering algorithms that optimize certain criterion functions, which measure key aspects of inter- and intra-cluster simil...
Ying Zhao, George Karypis
CORR
2010
Springer
131views Education» more  CORR 2010»
15 years 6 months ago
A PAC-Bayesian Analysis of Graph Clustering and Pairwise Clustering
We formulate weighted graph clustering as a prediction problem1 : given a subset of edge weights we analyze the ability of graph clustering to predict the remaining edge weights. ...
Yevgeny Seldin