Genetic Algorithms Applied to Multi-Class Clustering for Gene Expression Data

Genetic Algorithms Applied to Multi-Class Clustering for Gene Expression Data

A hybrid GA (genetic algorithm)-based clustering (HGACLUS) schema, combining merits of the Simulated Annealing, was described for finding an optimal or near-optimal set of medoids. This schema maximized the clustering success by achieving internal cluster cohesion and external cluster isolation. The performance of HGACLUS and other methods was compared by using simulated data and open microarra...

Genetic algorithms applied to multi-class prediction for the analysis of gene expression data

MOTIVATION An important challenge in the use of large-scale gene expression data for biological classification occurs when the expression dataset being analyzed involves multiple classes. Key issues that need to be addressed under such circumstances are the efficient selection of good predictive gene groups from datasets that are inherently 'noisy', and the development of new methodologies that...

Genetic Algorithms Applied to Clustering

In the following paper it is developed an image compression technique using clustering with genetic algorithms. This technique tries to avoid the problems associated with limitations from the operating system (MS-DOS), as Mariano O'kon [1] proposses in his tesis on genetic algorithms. The main problem, appears to be the large size of the data structures used by this kind of algorithms, which ca...

Genetic Algorithms applied to Clustering Problem and Data Mining

Clustering techniques have obtained adequate results when are applied to data mining problems. However, different runs of the same clustering technique on a specific dataset may result in different solutions. The cause of this difference is the choice of the initial cluster setting and the values of the parameters associated with the technique. A definition of good initial settings and optimal ...

Effective Clustering Algorithms for Gene Expression Data