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» Computing LTS Regression for Large Data Sets
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SIGMOD
2000
ACM
173views Database» more  SIGMOD 2000»
13 years 11 months ago
Efficient Algorithms for Mining Outliers from Large Data Sets
In this paper, we propose a novel formulation for distance-based outliers that is based on the distance of a point from its kth nearest neighbor. We rank each point on the basis o...
Sridhar Ramaswamy, Rajeev Rastogi, Kyuseok Shim
ICDM
2007
IEEE
116views Data Mining» more  ICDM 2007»
14 years 2 months ago
Privacy-Preserving k-NN for Small and Large Data Sets
It is not surprising that there is strong interest in kNN queries to enable clustering, classification and outlierdetection tasks. However, previous approaches to privacypreservi...
Artak Amirbekyan, Vladimir Estivill-Castro
ICDM
2009
IEEE
134views Data Mining» more  ICDM 2009»
13 years 5 months ago
Efficient Discovery of Confounders in Large Data Sets
Given a large transaction database, association analysis is concerned with efficiently finding strongly related objects. Unlike traditional associate analysis, where relationships ...
Wenjun Zhou, Hui Xiong
ICDE
2007
IEEE
119views Database» more  ICDE 2007»
14 years 2 months ago
Practical Preference Relations for Large Data Sets
User-defined preferences allow personalized ranking of query results. A user provides a declarative specification of his/her preferences, and the system is expected to use that ...
Kenneth A. Ross, Peter J. Stuckey, Amélie M...
ICRA
2010
IEEE
185views Robotics» more  ICRA 2010»
13 years 5 months ago
Heteroscedastic Gaussian processes for data fusion in large scale terrain modeling
This paper presents a novel approach to data fusion for stochastic processes that model spatial data. It addresses the problem of data fusion in the context of large scale terrain ...
Shrihari Vasudevan, Fabio T. Ramos, Eric Nettleton...