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KDD
2007
ACM
159views Data Mining» more  KDD 2007»
16 years 5 months ago
Constraint-driven clustering
Clustering methods can be either data-driven or need-driven. Data-driven methods intend to discover the true structure of the underlying data while need-driven methods aims at org...
Rong Ge, Martin Ester, Wen Jin, Ian Davidson
KDD
2007
ACM
148views Data Mining» more  KDD 2007»
16 years 5 months ago
Detecting research topics via the correlation between graphs and texts
In this paper we address the problem of detecting topics in large-scale linked document collections. Recently, topic detection has become a very active area of research due to its...
Yookyung Jo, Carl Lagoze, C. Lee Giles
KDD
2006
ACM
213views Data Mining» more  KDD 2006»
16 years 5 months ago
Learning sparse metrics via linear programming
Calculation of object similarity, for example through a distance function, is a common part of data mining and machine learning algorithms. This calculation is crucial for efficie...
Glenn Fung, Rómer Rosales
KDD
2005
ACM
140views Data Mining» more  KDD 2005»
16 years 5 months ago
Graphs over time: densification laws, shrinking diameters and possible explanations
How do real graphs evolve over time? What are "normal" growth patterns in social, technological, and information networks? Many studies have discovered patterns in stati...
Jure Leskovec, Jon M. Kleinberg, Christos Faloutso...
KDD
2004
ACM
163views Data Mining» more  KDD 2004»
16 years 5 months ago
Exploiting dictionaries in named entity extraction: combining semi-Markov extraction processes and data integration methods
We consider the problem of improving named entity recognition (NER) systems by using external dictionaries--more specifically, the problem of extending state-of-the-art NER system...
William W. Cohen, Sunita Sarawagi
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