Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
A multi-objective approach to discover biclusters in microarray data
Proceedings of the 9th annual conference on Genetic and evolutionary computation
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Applying biclustering to text mining: an immune-inspired approach
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Nearest-biclusters collaborative filtering with constant values
WebKDD'06 Proceedings of the 8th Knowledge discovery on the web international conference on Advances in web mining and web usage analysis
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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The biclustering technique was developed to avoid some of the drawbacks presented by standard clustering techniques. Given that biclustering requires the optimization of at least two conflicting objectives and that multiple independent solutions are desirable as the outcome, a few multi-objective evolutionary algorithms for biclustering were proposed in the literature. However, apart from the individual characteristics of the biclusters that should be optimized during their construction, several other global aspects should also be considered, such as the coverage of the dataset and the overlap among biclusters. These requirements will be addressed in this work with the MOM-aiNet+ algorithm, which is an improvement of the original multi-objective multipopulation artificial immune network denoted MOM-aiNet. Here, the MOM-aiNet+ algorithm will be described in detail, its main differences from the original MOM-aiNet will be highlighted, and both algorithms will be compared, together with three other proposals from the literature.