Sciweavers

922 search results - page 18 / 185
» A data mining approach to database compression
Sort
View
ICDM
2003
IEEE
140views Data Mining» more  ICDM 2003»
15 years 2 months ago
Mining Frequent Itemsets in Distributed and Dynamic Databases
Traditional methods for frequent itemset mining typically assume that data is centralized and static. Such methods impose excessive communication overhead when data is distributed...
Matthew Eric Otey, Chao Wang, Srinivasan Parthasar...
KDD
2004
ACM
209views Data Mining» more  KDD 2004»
15 years 10 months ago
A data mining approach to modeling relationships among categories in image collection
This paper proposes a data mining approach to modeling relationships among categories in image collection. In our approach, with image feature grouping, a visual dictionary is cre...
Ruofei Zhang, Zhongfei (Mark) Zhang, Sandeep Khanz...
AUSDM
2008
Springer
235views Data Mining» more  AUSDM 2008»
14 years 11 months ago
ShrFP-Tree: An Efficient Tree Structure for Mining Share-Frequent Patterns
Share-frequent pattern mining discovers more useful and realistic knowledge from database compared to the traditional frequent pattern mining by considering the non-binary frequen...
Chowdhury Farhan Ahmed, Syed Khairuzzaman Tanbeer,...
KDD
2009
ACM
198views Data Mining» more  KDD 2009»
15 years 10 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...
ICDE
2006
IEEE
176views Database» more  ICDE 2006»
15 years 3 months ago
Deriving Private Information from Perturbed Data Using IQR Based Approach
Several randomized techniques have been proposed for privacy preserving data mining of continuous data. These approaches generally attempt to hide the sensitive data by randomly m...
Songtao Guo, Xintao Wu, Yingjiu Li