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
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Interrelated Two-way Clustering: An Unsupervised Approach for Gene Expression Data Analysis
BIBE '01 Proceedings of the 2nd IEEE International Symposium on Bioinformatics and Bioengineering
Enhanced Biclustering on Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
HIS '07 Proceedings of the 7th International Conference on Hybrid Intelligent Systems
Applying Biclustering to Perform Collaborative Filtering
ISDA '07 Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications
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
Omni-aiNet: an immune-inspired approach for omni optimization
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
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
Query expansion using an immune-inspired biclustering algorithm
Natural Computing: an international journal
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
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Biclustering is a technique developed to allow simultaneous clustering of rows and columns of a dataset. This might be useful to extract more accurate information from sparse datasets and to avoid some of the drawbacks presented by standard clustering techniques, such as their impossibility of finding correlating data under a subset of features. Given that biclustering requires the optimization of two conflicting objectives (residue and volume) and that multiple independent solutions are desirable as the outcome, a multi-objective artificial immune system capable of performing a multipopulation search, named MOM-aiNet, will be proposed in this paper. To illustrate the capabilities of this novel algorithm, MOM-aiNet was applied to the extraction of biclusters from two datasets, one taken from a well-known gene expression problem and the other from a collaborative filtering application. A comparative analysis has also been accomplished, with the obtained results being confronted with the ones produced by two popular biclustering algorithms from the literature (FLOC and CC) and also by another immune-inspired approach for biclustering (BIC-aiNet).