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» Novel Algorithms for Privacy Preserving Utility Mining
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KDD
2007
ACM
159views Data Mining» more  KDD 2007»
14 years 7 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
SIGMOD
2001
ACM
200views Database» more  SIGMOD 2001»
14 years 7 months ago
Data Bubbles: Quality Preserving Performance Boosting for Hierarchical Clustering
In this paper, we investigate how to scale hierarchical clustering methods (such as OPTICS) to extremely large databases by utilizing data compression methods (such as BIRCH or ra...
Markus M. Breunig, Hans-Peter Kriegel, Peer Kr&oum...
ICDM
2003
IEEE
96views Data Mining» more  ICDM 2003»
14 years 19 days ago
Mining Plans for Customer-Class Transformation
We consider the problem of mining high-utility plans from historical plan databases that can be used to transform customers from one class to other, more desirable classes. Tradit...
Qiang Yang, Hong Cheng
CASCON
2004
129views Education» more  CASCON 2004»
13 years 8 months ago
Building predictors from vertically distributed data
Due in part to the large volume of data available today, but more importantly to privacy concerns, data are often distributed across institutional, geographical and organizational...
Sabine M. McConnell, David B. Skillicorn
ALT
2001
Springer
14 years 4 months ago
Inventing Discovery Tools: Combining Information Visualization with Data Mining
The growing use of information visualization tools and data mining algorithms stems from two separate lines of research. Information visualization researchers believe in the impor...
Ben Shneiderman