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Crossover Operator for Frequent Subgraph Mining
dc.contributor.advisor | Quirin, Arnaud | |
dc.contributor.advisor | Bahamonde Rionda, Antonio | |
dc.contributor.author | Beltrán Vargas, Juan Carlos | |
dc.date.accessioned | 2012-08-07T06:59:28Z | |
dc.date.available | 2012-08-07T06:59:28Z | |
dc.date.issued | 2012-07-25 | |
dc.identifier.uri | http://hdl.handle.net/10651/4182 | |
dc.description.abstract | Graph-based data mining approaches have been mainly proposed to the task popularly known as frequent subgraph mining subject to a single user pref- erence, like frequency, size, etc. In this work, I propose a new crossover oper- ator for frequent subgraph mining problem, where a subgraph (or solution) is defined by a genetic algorithm through several iterations, reproductions and filtering. I have develop a standard genetic algorithm, which includes most of the used stages as selection, crossover (without mutation), evalua- tion and replacement. Evolutionary algorithm for Graph-base data mining approaches is a very recent field, and the genetic algorithms for frequent subgraph mining subject is introduced in this project, with the proposal of a new crossover operator. This project is based in the framework of Subdue algorithm for subgraph mining. The method is called optimization by genetic algorithms (GAOptimize) and has several advantages: (i) optimization from a Subdue’s solutions stack in a single run (ii) selection of different constraints for substructure selection and reproduction (iii) search in the subgraphs lat- tice space and (iv) capability to deal with different isomorphic graph search algorithms. The good performance of GAOptimize is shown on two samples datasets from Subdue and two real-life datasets. | en |
dc.language.iso | eng | |
dc.relation.ispartofseries | Máster Universitario en Soft Computing y Análisis Inteligente de Datos | |
dc.rights | CC Reconocimiento - No comercial - Sin obras derivadas 3.0 España | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | |
dc.subject | Graph-Based Data Mining | en |
dc.subject | Frequent Subgraph Mining | en |
dc.subject | Subdue | spa |
dc.subject | Genetic Algorithms | en |
dc.subject | Evolutionary Optimization | en |
dc.subject | Crossover Operator | en |
dc.title | Crossover Operator for Frequent Subgraph Mining | eng |
dc.type | master thesis | spa |
dc.rights.accessRights | open access |
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