Papers by María José Martín Bautista
Lecture Notes in Computer Science, 2013
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Artificial Intelligence Review
The incursion of social media in our lives has been much accentuated in the last decade. This has... more The incursion of social media in our lives has been much accentuated in the last decade. This has led to a multiplication of data mining tools aimed at obtaining knowledge from these data sources. One of the greatest challenges in this area is to be able to obtain this knowledge without the need for training processes, which requires structured information and pre-labelled datasets. This is where unsupervised data mining techniques come in. These techniques can obtain value from these unstructured and unlabelled data, providing very interesting solutions to enhance the decision-making process. In this paper, we first address the problem of social media mining, as well as the need for unsupervised techniques, in particular association rules, for its treatment. We follow with a broad overview of the applications of association rules in the domain of social media mining, specifically, their application to the problems of mining textual entities, such as tweets. We also focus on the str...
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Applied Soft Computing
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Studies in fuzziness and soft computing, 2002
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IEEE Access, 2021
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IEEE Transactions on Industrial Informatics, 2021
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International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2018
This work presents an overview of the text mining area, considering the most common techniques, a... more This work presents an overview of the text mining area, considering the most common techniques, and including proposals based on the application of fuzzy sets. Besides, some of the most frequent text mining applications are mentioned. We discuss the existing approaches, which we call text data mining, in relation to the recently proposed paradigm of text knowledge mining, and we conclude that both are different and complementary, in the sense that they are able to extract different knowledge pieces from text by using different reasoning mechanisms. Future challenges related to text knowledge mining are also briefly outlined.
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IEEE Access, 2019
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Applied Soft Computing, 2016
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IEEE Transactions on Fuzzy Systems, 2016
This paper introduces Fuzzy HSS, a semisupervised hierarchical clustering approach that uses fuzz... more This paper introduces Fuzzy HSS, a semisupervised hierarchical clustering approach that uses fuzzy instance-level constraints. These constraints are external information on the shape of fuzzy must-link and fuzzy cannot-link restrictions. They allow uncertainty when indicating whether two instances of a dataset belong to the same group. Fuzzy must-link constraints give a degree of belief of two instances belonging to the same group. Analogously, fuzzy cannot-link constraints indicate the degree of belief of two instances not belonging to the same group. These constraints have been introduced in a hierarchical clustering process, allowing us to obtain the optimal number of groups in a dendrogram when the number of clusters is not known. The optimal amount of constraints needed in the process is determined by means of fuzzy entropy. An extensive experimental study is provided by comparing this fuzzy semisupervised approach with classic unsupervised methods, as well as a crisp semisupervised alternative.
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Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology, 2015
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Communications in Computer and Information Science, 2010
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Soft Computing, 2015
ABSTRACT In the last times, semi-supervised clustering has been an area that has received a lot o... more ABSTRACT In the last times, semi-supervised clustering has been an area that has received a lot of attention. It is distinguished from more traditional unsupervised approaches on the use of a small amount of supervision to “steer” clustering. Unfortunately in the real world, the supervision is not always available: data to process are often too large and so the cost (in terms of time and human resources) for user-provided information is not conceivable. To address this issue, this work presents an automatic generation of the supervision, by the analysis of the data structure itself. This analysis is performed using a partitional clustering algorithm that discovers relationships between pairs of instances that may be used as a semi-supervision in the clustering process. The methodology has been studied in the document clustering domain, an area where novel approaches for accurate documents classifications are strongly required. Experimental result shows the validity of this approach.
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Lecture Notes in Computer Science, 2009
... 3. Martın-Bautista, MJ, Martınez-Folgoso, S., Vila, M.: A New Semantic Representation for ...... more ... 3. Martın-Bautista, MJ, Martınez-Folgoso, S., Vila, M.: A New Semantic Representation for ... A., de Beuvron, F., Galea, D., Rousselot, F.: Using description logics for ontology extraction. ... In: Proceedings of the Conference of Language Resources and Evaluations (LREC 2004), pp. ...
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Studies in Fuzziness and Soft Computing, 2002
Google, Inc. (search). ...
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Studies in Fuzziness and Soft Computing, 2003
Google, Inc. (search). ...
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Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), 2000
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In this paper we review the main intermediate forms proposed in text mining, and we briefly study... more In this paper we review the main intermediate forms proposed in text mining, and we briefly study some fuzzy counterparts. The concept of intermediate form applies to any knowledge representation employed to represent in a structured way the semantic content of a text corpus. Intermediate forms play a central role in the text mining process since it is necessary to transform plain text into a form in order to apply mining techniques. Since the semantics of text use to be imprecise, the use of fuzzy intermediate forms seems to be a natural solution in many cases. We discuss about fuzzy intermediate forms and the corresponding fuzzy text mining techniques that may be applicable on them.
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Papers by María José Martín Bautista