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ICDM
2002
IEEE
159views Data Mining» more  ICDM 2002»
14 years 18 days ago
O-Cluster: Scalable Clustering of Large High Dimensional Data Sets
Clustering large data sets of high dimensionality has always been a serious challenge for clustering algorithms. Many recently developed clustering algorithms have attempted to ad...
Boriana L. Milenova, Marcos M. Campos
ICDM
2003
IEEE
100views Data Mining» more  ICDM 2003»
14 years 28 days ago
Tractable Group Detection on Large Link Data Sets
Discovering underlying structure from co-occurrence data is an important task in a variety of fields, including: insurance, intelligence, criminal investigation, epidemiology, hu...
Jeremy Kubica, Andrew W. Moore, Jeff G. Schneider
ICDM
2007
IEEE
116views Data Mining» more  ICDM 2007»
14 years 1 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
IDA
2011
Springer
13 years 2 months ago
A parallel, distributed algorithm for relational frequent pattern discovery from very large data sets
The amount of data produced by ubiquitous computing applications is quickly growing, due to the pervasive presence of small devices endowed with sensing, computing and communicatio...
Annalisa Appice, Michelangelo Ceci, Antonio Turi, ...