Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Clustering Algorithms
Gene discovery in leukemia revisited: a computational intelligence perspective
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Information Sciences: an International Journal
Rough set theory with discriminant analysis in analyzing electricity loads
Expert Systems with Applications: An International Journal
Roughfication of numeric decision tables: the case study of gene expression data
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
International Journal of Approximate Reasoning
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In many domains, the data objects are described in terms of a large number of features. The pipelined data mining approach introduced in [1] using two clustering algorithms in combination with rough sets and extended with genetic programming, is investigated with the purpose of discovering important subsets of attributes in high dimensional data. Their classification ability is described in terms of both collections of rules and analytic functions obtained by genetic programming (gene expression programming). The Leader and several k-means algorithms are used as procedures for attribute set simplification of the information systems later presented to rough sets algorithms. Visual data mining techniques including virtual reality were used for inspecting results. The data mining process is setup using high throughput distributed computing techniques. This approach was applied to Breast Cancer microarray data and it led to subsets of genes with high discrimination power with respect to the decision classes