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KDD
2007
ACM
159views Data Mining» more  KDD 2007»
14 years 11 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
182views Data Mining» more  KDD 2007»
14 years 11 months ago
Cleaning disguised missing data: a heuristic approach
In some applications such as filling in a customer information form on the web, some missing values may not be explicitly represented as such, but instead appear as potentially va...
Ming Hua, Jian Pei
KDD
2007
ACM
191views Data Mining» more  KDD 2007»
14 years 11 months ago
Cost-effective outbreak detection in networks
Given a water distribution network, where should we place sensors to quickly detect contaminants? Or, which blogs should we read to avoid missing important stories? These seemingl...
Andreas Krause, Carlos Guestrin, Christos Faloutso...
KDD
2007
ACM
151views Data Mining» more  KDD 2007»
14 years 11 months ago
Efficient mining of iterative patterns for software specification discovery
Studies have shown that program comprehension takes up to 45% of software development costs. Such high costs are caused by the lack-of documented specification and further aggrava...
Chao Liu 0001, David Lo, Siau-Cheng Khoo
KDD
2006
ACM
164views Data Mining» more  KDD 2006»
14 years 11 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
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