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» Efficient Privacy Preserving K-Means Clustering
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TDP
2010
166views more  TDP 2010»
12 years 9 months ago
Communication-Efficient Privacy-Preserving Clustering
The ability to store vast quantities of data and the emergence of high speed networking have led to intense interest in distributed data mining. However, privacy concerns, as well ...
Geetha Jagannathan, Krishnan Pillaipakkamnatt, Reb...
PAISI
2010
Springer
13 years 21 days ago
Efficient Privacy Preserving K-Means Clustering
Abstract. This paper introduces an efficient privacy-preserving protocol for distributed K-means clustering over an arbitrary partitioned data, shared among N parties. Clustering i...
Maneesh Upmanyu, Anoop M. Namboodiri, Kannan Srina...
ICDM
2009
IEEE
133views Data Mining» more  ICDM 2009»
13 years 9 months ago
On K-Means Cluster Preservation Using Quantization Schemes
This work examines under what conditions compression methodologies can retain the outcome of clustering operations. We focus on the popular k-Means clustering algorithm and we dem...
Deepak S. Turaga, Michail Vlachos, Olivier Versche...
GRC
2007
IEEE
13 years 9 months ago
Towards Mobile Internet: Location Privacy Threats and Granular Computation Challenges
The bulk of contents out on the Internet continue to grow at an astounding pace. As computing and communications become ubiquitous, we are entering the Mobile Internet Computing e...
Ling Liu
KDD
2007
ACM
159views Data Mining» more  KDD 2007»
14 years 3 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