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146
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PAISI
2010
Springer
15 years 20 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...
134
Voted
IJCM
2007
205views more  IJCM 2007»
15 years 2 months ago
BTF modelling using BRDF texels
The highest fidelity representations of realistic real-world materials currently used comprise Bidirectional Texture Functions (BTF). The BTF is a six dimensional function dependi...
Jirí Filip, Michal Haindl
134
Voted
OSDI
2008
ACM
16 years 3 months ago
Hunting for Problems with Artemis
Artemis is a modular application designed for analyzing and troubleshooting the performance of large clusters running datacenter services. Artemis is composed of four modules: (1)...
Gabriela F. Cretu-Ciocarlie, Mihai Budiu, Mois&eac...
145
Voted
SDM
2011
SIAM
183views Data Mining» more  SDM 2011»
14 years 5 months ago
Nonparametric Bayesian Co-clustering Ensembles
A nonparametric Bayesian approach to co-clustering ensembles is presented. Similar to clustering ensembles, coclustering ensembles combine various base co-clustering results to ob...
Pu Wang, Kathryn B. Laskey, Carlotta Domeniconi, M...
134
Voted
ICDM
2009
IEEE
137views Data Mining» more  ICDM 2009»
15 years 9 months ago
A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks
This paper offers a local distributed algorithm for expectation maximization in large peer-to-peer environments. The algorithm can be used for a variety of well-known data mining...
Kanishka Bhaduri, Ashok N. Srivastava