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HAIS
2011
Springer
12 years 11 months ago
Evolving Temporal Fuzzy Association Rules from Quantitative Data with a Multi-Objective Evolutionary Algorithm
A novel method for mining association rules that are both quantitative and temporal using a multi-objective evolutionary algorithm is presented. This method successfully identifie...
Stephen G. Matthews, Mario A. Góngora, Adri...
HAIS
2009
Springer
13 years 11 months ago
Evolutionary Extraction of Association Rules: A Preliminary Study on their Effectiveness
Abstract. Data Mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binaryvalued transactions, however the d...
Nicolò Flugy Papè, Jesús Alca...
SIGMOD
1998
ACM
233views Database» more  SIGMOD 1998»
13 years 11 months ago
Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications
Data mining applications place special requirements on clustering algorithms including: the ability to nd clusters embedded in subspaces of high dimensional data, scalability, end...
Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopul...
ICANN
2009
Springer
13 years 5 months ago
Mining Rules for the Automatic Selection Process of Clustering Methods Applied to Cancer Gene Expression Data
Different algorithms have been proposed in the literature to cluster gene expression data, however there is no single algorithm that can be considered the best one independently on...
André C. A. Nascimento, Ricardo Bastos Cava...
ICDM
2003
IEEE
107views Data Mining» more  ICDM 2003»
14 years 25 days ago
Objective and Subjective Algorithms for Grouping Association Rules
We propose two algorithms for grouping and summarizing association rules. The first algorithm recursively groups rules according to the structure of the rules and generates a tre...
Aijun An, Shakil M. Khan, Xiangji Huang