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» A Genetic Algorithm for Clustering on Very Large Data Sets
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ISNN
2004
Springer
14 years 2 months ago
Sparse Bayesian Learning Based on an Efficient Subset Selection
Based on rank-1 update, Sparse Bayesian Learning Algorithm (SBLA) is proposed. SBLA has the advantages of low complexity and high sparseness, being very suitable for large scale pr...
Liefeng Bo, Ling Wang, Licheng Jiao
ICML
2007
IEEE
14 years 9 months ago
Maximum margin clustering made practical
Maximum margin clustering (MMC) is a recent large margin unsupervised learning approach that has often outperformed conventional clustering methods. Computationally, it involves n...
Kai Zhang, Ivor W. Tsang, James T. Kwok
DMIN
2006
144views Data Mining» more  DMIN 2006»
13 years 10 months ago
Discovering Assignment Rules in Workforce Schedules Using Data Mining
Discovering hidden patterns in large sets of workforce schedules to gain insight into the potential knowledge in workforce schedules are crucial to better understanding the workfor...
Jihong Yan
ICDE
2007
IEEE
129views Database» more  ICDE 2007»
14 years 3 months ago
Ontology-driven Rule Generalization and Categorization for Market Data
—Radio Frequency Identification (RFID) is an emerging technique that can significantly enhance supply chain processes and deliver customer service improvements. RFID provides use...
Dongwoo Won, Dennis McLeod
VLDB
1998
ACM
105views Database» more  VLDB 1998»
14 years 28 days ago
Computing Iceberg Queries Efficiently
Many applications compute aggregate functions over an attribute (or set of attributes) to find aggregate values above some specified threshold. We call such queries iceberg querie...
Min Fang, Narayanan Shivakumar, Hector Garcia-Moli...