Palero, Fernando; Palero, Antonio Gonzalez-Pardo Fernando; Camacho, David
Simple gamer interaction analysis through tower defense games Conference
6th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI 2014), Lecture Notes in Artificial Intelligence of Springer-Verlag, 2015.
@conference{2015-PaleroEtAl,
title = {Simple gamer interaction analysis through tower defense games},
author = {Fernando Palero and Antonio Gonzalez-Pardo Fernando Palero and David Camacho},
year = {2015},
date = {2015-01-01},
booktitle = {6th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI 2014)},
pages = {185-194},
publisher = {Lecture Notes in Artificial Intelligence of Springer-Verlag},
keywords = {Data Analysis, Data Mining, Videogames},
pubstate = {published},
tppubtype = {conference}
}
@incollection{springerlink:10.1007/978-3-642-32639-4_27,
title = {A Genetic Graph-Based Clustering Algorithm},
author = {Héctor Menéndez and David Camacho},
editor = {Hujun Yin and José Costa and Guilherme Barreto},
url = {http://dx.doi.org/10.1007/978-3-642-32639-4_27},
issn = {978-3-642-32638-7},
year = {2012},
date = {2012-01-01},
booktitle = {Intelligent Data Engineering and Automated Learning - IDEAL 2012},
volume = {7435},
pages = {216-225},
publisher = {Springer Berlin / Heidelberg},
series = {Lecture Notes in Computer Science},
abstract = {The interest in the analysis and study of clustering techniques have grown since the introduction of new algorithms based on the continuity of the data, where problems related to image segmentation and tracking, amongst others, makes difficult the correct classification of data into their appropriate groups, or clusters. Some new techniques, such as Spectral Clustering (SC), uses graph theory to generate the clusters through the spectrum of the graph created by a similarity function applied to the elements of the database. The approach taken by SC allows to handle the problem of data continuity though the graph representation. Based on this idea, this study uses genetic algorithms to select the groups using the same similarity graph built by the Spectral Clustering method. The main contribution is to create a new algorithm which improves the robustness of the Spectral Clustering algorithm reducing the dependency of the similarity metric parameters that currently affects to the performance of SC approaches. This algorithm, named Genetic Graph-based Clustering (GGC), has been tested with different synthetic and real-world datasets, the experimental results have been compared against classical clustering algorithms like K-Means, EM and SC.},
note = {10.1007/978-3-642-32639-4_27},
keywords = {clustering techniques, Data Analysis, Data Mining, Graph Theory},
pubstate = {published},
tppubtype = {incollection}
}
The interest in the analysis and study of clustering techniques have grown since the introduction of new algorithms based on the continuity of the data, where problems related to image segmentation and tracking, amongst others, makes difficult the correct classification of data into their appropriate groups, or clusters. Some new techniques, such as Spectral Clustering (SC), uses graph theory to generate the clusters through the spectrum of the graph created by a similarity function applied to the elements of the database. The approach taken by SC allows to handle the problem of data continuity though the graph representation. Based on this idea, this study uses genetic algorithms to select the groups using the same similarity graph built by the Spectral Clustering method. The main contribution is to create a new algorithm which improves the robustness of the Spectral Clustering algorithm reducing the dependency of the similarity metric parameters that currently affects to the performance of SC approaches. This algorithm, named Genetic Graph-based Clustering (GGC), has been tested with different synthetic and real-world datasets, the experimental results have been compared against classical clustering algorithms like K-Means, EM and SC.
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, pp. 20:1–20:9, ACM, New York, NY, USA, 2012, ISSN: 978-1-4503-0915-8.
@inproceedings{Menendez:2012:FSH:2254129.2254155,
title = {Features selection from high-dimensional web data using clustering analysis},
author = {Hector Menendez and Gema Bello-Orgaz and David Camacho},
url = {http://doi.acm.org/10.1145/2254129.2254155},
issn = {978-1-4503-0915-8},
year = {2012},
date = {2012-01-01},
booktitle = {Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics},
pages = {20:1--20:9},
publisher = {ACM},
address = {New York, NY, USA},
series = {WIMS '12},
keywords = {cup, data, Data Analysis, FIFA, football, mining, projection, soccer, techniques, world},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{DBLP:conf/ideal/GranadosMCR10,
title = {Relevance of Contextual Information in Compression-Based Text Clustering},
author = {Ana Granados and Rafael Martínez and David Camacho and Francisco Borja de Rodríguez},
year = {2010},
date = {2010-01-01},
booktitle = {IDEAL},
pages = {259-266},
crossref = {DBLP:conf/ideal/2010},
keywords = {Clustering, Data Analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
@article{DBLP:journals/corr/abs-0711-4075,
title = {Evaluating the Impact of Information Distortion on Normalized Compression Distance-driven Text Clustering},
author = {Ana Granados and Manuel Cebrián and David Camacho and Francisco Borja de Rodríguez},
year = {2007},
date = {2007-01-01},
journal = {CoRR},
volume = {abs/0711.4075},
keywords = {Clustering, Data Analysis},
pubstate = {published},
tppubtype = {article}
}
