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» Clustering Improves the Exploration of Graph Mining Results
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KBS
2006
150views more  KBS 2006»
14 years 11 months ago
Clusterer ensemble
Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. This paper explores ensemble methods ...
Zhi-Hua Zhou, Wei Tang
SIGMOD
1999
ACM
183views Database» more  SIGMOD 1999»
15 years 4 months ago
OPTICS: Ordering Points To Identify the Clustering Structure
Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysi...
Mihael Ankerst, Markus M. Breunig, Hans-Peter Krie...
ICDE
2005
IEEE
164views Database» more  ICDE 2005»
16 years 1 months ago
Knowledge Discovery from Transportation Network Data
Transportation and Logistics are a major sector of the economy, however data analysis in this domain has remained largely in the province of optimization. The potential of data mi...
Wei Jiang, Jaideep Vaidya, Zahir Balaporia, Chris ...
CIKM
2009
Springer
15 years 6 months ago
Independent informative subgraph mining for graph information retrieval
In order to enable scalable querying of graph databases, intelligent selection of subgraphs to index is essential. An improved index can reduce response times for graph queries si...
Bingjun Sun, Prasenjit Mitra, C. Lee Giles
KDD
2007
ACM
181views Data Mining» more  KDD 2007»
16 years 4 days ago
BoostCluster: boosting clustering by pairwise constraints
Data clustering is an important task in many disciplines. A large number of studies have attempted to improve clustering by using the side information that is often encoded as pai...
Yi Liu, Rong Jin, Anil K. Jain