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» Clustering Improves the Exploration of Graph Mining Results
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ICML
2005
IEEE
16 years 16 days ago
Semi-supervised graph clustering: a kernel approach
Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are...
Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Ray...
ICDM
2005
IEEE
163views Data Mining» more  ICDM 2005»
15 years 5 months ago
Balancing Exploration and Exploitation: A New Algorithm for Active Machine Learning
Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert)...
Thomas Takeo Osugi, Kun Deng, Stephen D. Scott
BPM
2008
Springer
192views Business» more  BPM 2008»
15 years 1 months ago
Trace Clustering in Process Mining
Process mining has proven to be a valuable tool for analyzing operational process executions based on event logs. Existing techniques perform well on structured processes, but stil...
Minseok Song, Christian W. Günther, Wil M. P....
KDD
2004
ACM
145views Data Mining» more  KDD 2004»
16 years 4 days ago
Mining coherent gene clusters from gene-sample-time microarray data
Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications. In this paper, we explore a novel type of genesa...
Daxin Jiang, Jian Pei, Murali Ramanathan, Chun Tan...
EDBT
2010
ACM
170views Database» more  EDBT 2010»
15 years 3 months ago
Augmenting OLAP exploration with dynamic advanced analytics
Online Analytical Processing (OLAP) is a popular technique for explorative data analysis. Usually, a fixed set of dimensions (such as time, place, etc.) is used to explore and ana...
Benjamin Leonhardi, Bernhard Mitschang, Rubé...