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» Efficient Algorithms for Discovering Association Rules
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FQAS
2004
Springer
146views Database» more  FQAS 2004»
14 years 15 days ago
Discovering Representative Models in Large Time Series Databases
The discovery of frequently occurring patterns in a time series could be important in several application contexts. As an example, the analysis of frequent patterns in biomedical ...
Simona E. Rombo, Giorgio Terracina
SIGMOD
2005
ACM
161views Database» more  SIGMOD 2005»
14 years 9 months ago
Mining Top-k Covering Rule Groups for Gene Expression Data
In this paper, we propose a novel algorithm to discover the topk covering rule groups for each row of gene expression profiles. Several experiments on real bioinformatics datasets...
Gao Cong, Kian-Lee Tan, Anthony K. H. Tung, Xin Xu
CIBCB
2007
IEEE
14 years 23 days ago
Associative Artificial Neural Network for Discovery of Highly Correlated Gene Groups Based on Gene Ontology and Gene Expression
Abstract-- The advance of high-throughput experimental technologies poses continuous challenges to computational data analysis in functional and comparative genomics studies. Gene ...
Ji He, Xinbin Dai, Xuechun Zhao
CIKM
2004
Springer
14 years 2 months ago
Discovering frequently changing structures from historical structural deltas of unordered XML
Recently, a large amount of work has been done in XML data mining. However, we observed that most of the existing works focus on the snapshot XML data, while XML data is dynamic i...
Qiankun Zhao, Sourav S. Bhowmick, Mukesh K. Mohani...
VLDB
2004
ACM
115views Database» more  VLDB 2004»
14 years 2 months ago
Semantic Mining and Analysis of Gene Expression Data
Association rules can reveal biological relevant relationship between genes and environments / categories. However, most existing association rule mining algorithms are rendered i...
Xin Xu, Gao Cong, Beng Chin Ooi, Kian-Lee Tan, Ant...