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PAISI
2010
Springer
14 years 9 months 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...
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
16 years 8 days ago
Practical learning from one-sided feedback
In many data mining applications, online labeling feedback is only available for examples which were predicted to belong to the positive class. Such applications include spam filt...
D. Sculley
KDD
2007
ACM
182views Data Mining» more  KDD 2007»
16 years 8 days ago
A fast algorithm for finding frequent episodes in event streams
Frequent episode discovery is a popular framework for mining data available as a long sequence of events. An episode is essentially a short ordered sequence of event types and the...
Srivatsan Laxman, P. S. Sastry, K. P. Unnikrishnan
KDD
2006
ACM
145views Data Mining» more  KDD 2006»
16 years 8 days ago
Deriving quantitative models for correlation clusters
Correlation clustering aims at grouping the data set into correlation clusters such that the objects in the same cluster exhibit a certain density and are all associated to a comm...
Arthur Zimek, Christian Böhm, Elke Achtert, H...
SIGMOD
2008
ACM
107views Database» more  SIGMOD 2008»
16 years 18 hour ago
Outlier-robust clustering using independent components
How can we efficiently find a clustering, i.e. a concise description of the cluster structure, of a given data set which contains an unknown number of clusters of different shape ...
Christian Böhm, Christos Faloutsos, Claudia P...