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KDD
2007
ACM
165views Data Mining» more  KDD 2007»
14 years 8 months ago
Finding low-entropy sets and trees from binary data
The discovery of subsets with special properties from binary data has been one of the key themes in pattern discovery. Pattern classes such as frequent itemsets stress the co-occu...
Eino Hinkkanen, Hannes Heikinheimo, Heikki Mannila...
KDD
2006
ACM
156views Data Mining» more  KDD 2006»
14 years 8 months ago
Discovering significant OPSM subspace clusters in massive gene expression data
Order-preserving submatrixes (OPSMs) have been accepted as a biologically meaningful subspace cluster model, capturing the general tendency of gene expressions across a subset of ...
Byron J. Gao, Obi L. Griffith, Martin Ester, Steve...
KDD
2005
ACM
165views Data Mining» more  KDD 2005»
14 years 8 months ago
Co-clustering by block value decomposition
Dyadic data matrices, such as co-occurrence matrix, rating matrix, and proximity matrix, arise frequently in various important applications. A fundamental problem in dyadic data a...
Bo Long, Zhongfei (Mark) Zhang, Philip S. Yu
KDD
2004
ACM
164views Data Mining» more  KDD 2004»
14 years 8 months ago
Discovering complex matchings across web query interfaces: a correlation mining approach
To enable information integration, schema matching is a critical step for discovering semantic correspondences of attributes across heterogeneous sources. While complex matchings ...
Bin He, Kevin Chen-Chuan Chang, Jiawei Han
SIGMOD
2008
ACM
142views Database» more  SIGMOD 2008»
14 years 7 months ago
Cost-based variable-length-gram selection for string collections to support approximate queries efficiently
Approximate queries on a collection of strings are important in many applications such as record linkage, spell checking, and Web search, where inconsistencies and errors exist in...
Xiaochun Yang, Bin Wang, Chen Li