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KDD
2009
ACM
198views Data Mining» more  KDD 2009»
14 years 8 months ago
Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data
All Netflix Prize algorithms proposed so far are prohibitively costly for large-scale production systems. In this paper, we describe an efficient dataflow implementation of a coll...
Srivatsava Daruru, Nena M. Marin, Matt Walker, Joy...
ICS
2009
Tsinghua U.
14 years 1 days ago
A translation system for enabling data mining applications on GPUs
Modern GPUs offer much computing power at a very modest cost. Even though CUDA and other related recent developments are accelerating the use of GPUs for general purpose applicati...
Wenjing Ma, Gagan Agrawal
EDBT
2004
ACM
234views Database» more  EDBT 2004»
14 years 7 months ago
A Condensation Approach to Privacy Preserving Data Mining
In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. In many...
Charu C. Aggarwal, Philip S. Yu
HPCA
2006
IEEE
14 years 7 months ago
Last level cache (LLC) performance of data mining workloads on a CMP - a case study of parallel bioinformatics workloads
With the continuing growth in the amount of genetic data, members of the bioinformatics community are developing a variety of data-mining applications to understand the data and d...
Aamer Jaleel, Matthew Mattina, Bruce L. Jacob
ICDCS
2002
IEEE
14 years 11 days ago
A Fully Distributed Framework for Cost-Sensitive Data Mining
Data mining systems aim to discover patterns and extract useful information from facts recorded in databases. A widely adopted approach is to apply machine learning algorithms to ...
Wei Fan, Haixun Wang, Philip S. Yu, Salvatore J. S...