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KDD
2006
ACM
164views Data Mining» more  KDD 2006»
16 years 7 months ago
Sampling from large graphs
Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.)...
Jure Leskovec, Christos Faloutsos
KDD
2005
ACM
157views Data Mining» more  KDD 2005»
16 years 7 months ago
A fast kernel-based multilevel algorithm for graph clustering
Graph clustering (also called graph partitioning) -- clustering the nodes of a graph -- is an important problem in diverse data mining applications. Traditional approaches involve...
Inderjit S. Dhillon, Yuqiang Guan, Brian Kulis
KDD
2005
ACM
165views Data Mining» more  KDD 2005»
16 years 7 months ago
Co-clustering by block value decomposition
Dyadic data matrices, such as co-occurrence matrix, rating matrix, and proximity matrix, arise frequently in various important applications. A fundamental problem in dyadic data a...
Bo Long, Zhongfei (Mark) Zhang, Philip S. Yu
KDD
2005
ACM
104views Data Mining» more  KDD 2005»
16 years 7 months ago
A hit-miss model for duplicate detection in the WHO drug safety database
The WHO Collaborating Centre for International Drug Monitoring in Uppsala, Sweden, maintains and analyses the world's largest database of reports on suspected adverse drug re...
Andrew Bate, G. Niklas Norén, Roland Orre
KDD
2005
ACM
153views Data Mining» more  KDD 2005»
16 years 7 months ago
Improving discriminative sequential learning with rare--but--important associations
Discriminative sequential learning models like Conditional Random Fields (CRFs) have achieved significant success in several areas such as natural language processing, information...
Xuan Hieu Phan, Minh Le Nguyen, Tu Bao Ho, Susumu ...
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