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» Efficient Discovery of Confounders in Large Data Sets
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PAKDD
2005
ACM
128views Data Mining» more  PAKDD 2005»
14 years 1 months ago
A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets
Traditional association rules mining cannot meet the demands arising from some real applications. By considering the different values of individual items as utilities, utility mini...
Ying Liu, Wei-keng Liao, Alok N. Choudhary
ACSC
2003
IEEE
13 years 11 months ago
Efficient Trie-Based Sorting of Large Sets of Strings
Sorting is a fundamental algorithmic task. Many generalpurpose sorting algorithms have been developed, but efficiency gains can be achieved by designing algorithms for specific ki...
Ranjan Sinha, Justin Zobel
ICMLA
2008
13 years 9 months ago
An Improved Generalized Discriminant Analysis for Large-Scale Data Set
In order to overcome the computation and storage problem for large-scale data set, an efficient iterative method of Generalized Discriminant Analysis is proposed. Because sample v...
Weiya Shi, Yue-Fei Guo, Cheng Jin, Xiangyang Xue
ICDE
2003
IEEE
149views Database» more  ICDE 2003»
14 years 9 months ago
Indexing Weighted-Sequences in Large Databases
We present an index structure for managing weightedsequences in large databases. A weighted-sequence is defined as a two-dimensional structure where each element in the sequence i...
Haixun Wang, Chang-Shing Perng, Wei Fan, Sanghyun ...
DMKD
1997
ACM
308views Data Mining» more  DMKD 1997»
13 years 11 months ago
A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining
Partitioning a large set of objects into homogeneous clusters is a fundamental operation in data mining. The k-means algorithm is best suited for implementing this operation becau...
Zhexue Huang