Publications

@conference{Díaz-Díaz2011b,

title = {Gene-gene interaction based clustering method for microarray data},

author = {N. Díaz-Díaz and F. Gómez-Vela and J. Aguilar-Ruiz and J. García-Gutiérrez},

doi = {10.1109/ISDA.2011.6121800},

issn = {2164-7151},

year  = {2011},

date = {2011-11-24},

booktitle = {2011 11th International Conference on Intelligent Systems Design and Applications},

pages = {1067-1073},

abstract = {In this paper, we propose a greedy clustering algorithm to identify groups of related genes and a new measure to improve the results of this algorithm. Clustering algorithms analyze genes in order to group those with similar behavior. Instead, our approach groups pairs of genes that present similar positive and/or negative interactions. In order to avoid noise in clusters, we apply a threshold, the neighbouring minimun index(?), to know if a pair of genes have interaction enough or not. The algorithm allows the researcher to modify all the criteria: discretization mapping function, gene-gene mapping function and filtering function, and even the neighbouring minimun index, and provides much flexibility to obtain clusters based on the level of precision needed. We have carried out a deep experimental study in databases to obtain a good neighbouring minimun index, ?. The performance of our approach is experimentally tested on the yeast, yeast cell-cycle and malaria datasets. The final number of clusters has a very high level of customization and genes within show a significant level of cohesion, as it is shown graphically in the experiments.},

keywords = {Clustering},

pubstate = {published},

tppubtype = {conference}

}

@conference{Aguilar-Ruiz2003,

title = {Clustering Main Concepts from e-Mails},

author = {J. Aguilar-Ruiz and D. Rodríguez-Baena and P. R. Cohen and J. C. Riquelme},

doi = {10.1007/978-3-540-25945-9_23},

isbn = {978-3-540-25945-9},

year  = {2003},

date = {2003-01-01},

booktitle = {Current Topics in Artificial Intelligence},

pages = {231-240},

abstract = {E–mail is one of the most common ways to communicate, assuming, in some cases, up to 75% of a company’s communication, in which every employee spends about 90 minutes a day in e–mail tasks such as filing and deleting. This paper deals with the generation of clusters of relevant words from E–mail texts. Our approach consists of the application of text mining techniques and, later, data mining techniques, to obtain related concepts extracted from sent and received messages. We have developed a new clustering algorithm based on neighborhood, which takes into account similarity values among words obtained in the text mining phase. The potential of these applications is enormous and only a few companies, mainly large organizations, have invested in this project so far, taking advantage of employees’s knowledge in future decisions.},

keywords = {Clustering},

pubstate = {published},

tppubtype = {conference}

}