Algorithms for clustering data
Algorithms for clustering data
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
ACM Computing Surveys (CSUR)
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
On Clustering Validation Techniques
Journal of Intelligent Information Systems
IEEE Transactions on Visualization and Computer Graphics
DHC: A Density-Based Hierarchical Clustering Method for Time Series Gene Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Foundations of Soft Case-Based Reasoning
Foundations of Soft Case-Based Reasoning
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Cluster Analysis for Gene Expression Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
A genetic algorithm that exchanges neighboring centers for k-means clustering
Pattern Recognition Letters
Parallel Clustering Algorithm for Large Data Sets with Applications in Bioinformatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Inferential Clustering Approach for Microarray Experiments with Replicated Measurements
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Modeling and Visualizing Uncertainty in Gene Expression Clusters Using Dirichlet Process Mixtures
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Advanced Data Mining Techniques
Advanced Data Mining Techniques
Clustering problem using adaptive genetic algorithm
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Evolutionary computation in bioinformatics: a review
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An evolutionary clustering algorithm for gene expression microarray data analysis
IEEE Transactions on Evolutionary Computation
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
IEEE Transactions on Evolutionary Computation
A Runtime Analysis of Evolutionary Algorithms for Constrained Optimization Problems
IEEE Transactions on Evolutionary Computation
Visualizing fuzzy points in parallel coordinates
IEEE Transactions on Fuzzy Systems
| Hi-index | 12.05 |
This paper proposes a new hierarchical clustering method using genetic algorithms for the analysis of gene expression data. This method is based on the mathematical proof of several results, showing its effectiveness with regard to other clustering methods. Genetic algorithms applied to cluster analysis have disclosed good results on biological data and many studies have been carried out in this sense, although most of them are focused on partitional clustering methods. Even though there are few studies that attempt to use genetic algorithms for building hierarchical clustering, they do not include constraints that allow us to reduce the complexity of the problem. Therefore, these studies become intractable problems for large data sets. On the other hand, the deterministic hierarchical clustering methods generally face the problem of convergence towards local optimums due to their greedy strategy. The method introduced here is an alternative to solve some of the problems existing methods face. The results of the experiments have shown that our approach can be very effective in cluster analysis of DNA microarray data.