Data mining and knowledge discovery in databases
Communications of the ACM
Enhancements to the data mining process
Enhancements to the data mining process
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Attribute Selection with a Multi-objective Genetic Algorithm
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Heuristics and meta-heuristics for 2-layer straight line crossing minimization
Discrete Applied Mathematics
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Evolutionary model selection in unsupervised learning
Intelligent Data Analysis
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Data mining involves useful knowledge discovery using a data matrix consisting of records and attributes or variables. Not all the attributes may be useful in knowledge discovery, as some of them may be redundant, irrelevant, noisy or even opposing. Furthermore, using all the attributes increases the complexity of solving the problem. The Minimum Attribute Subset Selection Problem (MASSP) has been studied for well over three decades and researchers have come up with several solutions In this paper a new technique is proposed for the MASSP based on the crossing minimization paradigm from the domain of graph drawing using biclustering. Biclustering is used to quickly identify those attributes that are significant in the data matrix. The attributes identified are then used to perform one-way clustering and generate pixelized visualization of the clustered results. Using the proposed technique on two real datasets has shown promising results.