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Daniel Sánchez

Bio: Daniel Sánchez is an academic researcher from University of Granada. The author has contributed to research in topics: Fuzzy logic & Fuzzy set. The author has an hindex of 28, co-authored 168 publications receiving 3121 citations. Previous affiliations of Daniel Sánchez include Roskilde University & Andalusian School of Public Health.


Papers
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Journal ArticleDOI
TL;DR: A general model to discover association rules among items in a (crisp) set of fuzzy transactions is developed, which can be particularized in several ways; each particular instance corresponds to a certain kind of pattern and/or repository of data.
Abstract: The theory of fuzzy sets has been recognized as a suitable tool to model several kinds of patterns that can hold in data. In this paper, we are concerned with the development of a general model to discover association rules among items in a (crisp) set of fuzzy transactions. This general model can be particularized in several ways; each particular instance corresponds to a certain kind of pattern and/or repository of data. We describe some applications of this scheme, paying special attention to the discovery of fuzzy association rules in relational databases.

320 citations

Journal ArticleDOI
TL;DR: Association rules are proposed to be used in order to extract knowledge so that normal behavior patterns may be obtained in unlawful transactions from transactional credit card databases inorder to detect and prevent fraud.
Abstract: Association rules are considered to be the best studied models for data mining. In this article, we propose their use in order to extract knowledge so that normal behavior patterns may be obtained in unlawful transactions from transactional credit card databases in order to detect and prevent fraud. The proposed methodology has been applied on data about credit card fraud in some of the most important retail companies in Chile.

238 citations

Journal ArticleDOI
TL;DR: A set of properties that must be fulfilled by any method of evaluation of quantified statements is proposed, and a general approach for the evaluation ofquantified statements based on the fuzzy cardinality and fuzzy relative cardinality of fuzzy sets is described.

178 citations

Journal ArticleDOI
01 Jan 2002
TL;DR: A different framework is introduced, based on Shortliffe and Buchanan's certainty factors and the new concept of very strong rules, and some intuitive properties of the new framework are discussed, showing that it can avoid the discovery of misleading rules, improving the manageability and quality of the results.
Abstract: It has been pointed out that the usual framework to assess association rules, based on support and confidence as measures of importance and accuracy, has several drawbacks. In particular, the presence of items with very high support can lead to obtain many misleading rules, even in the order of 95% of the discovered rules in some of our experiments. In this paper we introduce a different framework, based on Shortliffe and Buchanan's certainty factors and the new concept of very strong rules, and we discuss some intuitive properties of the new framework. Both the theoretical properties and the experiments we have performed show that we can avoid the discovery of misleading rules, improving the manageability and quality of the results.

150 citations

Journal ArticleDOI
TL;DR: This paper is intended to offer a global view of the development on this branch until now, focusing in the different approaches dealing with quantification, specially those involving imprecision, called fuzzy quantification.

105 citations


Cited by
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01 Jan 2002

9,314 citations

Journal ArticleDOI
TL;DR: It is proved that the envelope of the hesitant fuzzy sets is an intuitionistic fuzzy set, and it is proved also that the operations proposed are consistent with the ones of intuitionist fuzzy sets when applied to the envelope.
Abstract: Several extensions and generalizations of fuzzy sets have been introduced in the literature, for example, Atanassov's intuitionistic fuzzy sets, type 2 fuzzy sets, and fuzzy multisets. In this paper, we propose hesitant fuzzy sets. Although from a formal point of view, they can be seen as fuzzy multisets, we will show that their interpretation differs from the two existing approaches for fuzzy multisets. Because of this, together with their definition, we also introduce some basic operations. In addition, we also study their relationship with intuitionistic fuzzy sets. We prove that the envelope of the hesitant fuzzy sets is an intuitionistic fuzzy set. We prove also that the operations we propose are consistent with the ones of intuitionistic fuzzy sets when applied to the envelope of the hesitant fuzzy sets. © 2010 Wiley Periodicals, Inc.

2,232 citations

Journal ArticleDOI
01 Feb 2011
TL;DR: The main data mining techniques used for FFD are logistic models, neural networks, the Bayesian belief network, and decision trees, all of which provide primary solutions to the problems inherent in the detection and classification of fraudulent data.
Abstract: This paper presents a review of - and classification scheme for - the literature on the application of data mining techniques for the detection of financial fraud. Although financial fraud detection (FFD) is an emerging topic of great importance, a comprehensive literature review of the subject has yet to be carried out. This paper thus represents the first systematic, identifiable and comprehensive academic literature review of the data mining techniques that have been applied to FFD. 49 journal articles on the subject published between 1997 and 2008 was analyzed and classified into four categories of financial fraud (bank fraud, insurance fraud, securities and commodities fraud, and other related financial fraud) and six classes of data mining techniques (classification, regression, clustering, prediction, outlier detection, and visualization). The findings of this review clearly show that data mining techniques have been applied most extensively to the detection of insurance fraud, although corporate fraud and credit card fraud have also attracted a great deal of attention in recent years. In contrast, we find a distinct lack of research on mortgage fraud, money laundering, and securities and commodities fraud. The main data mining techniques used for FFD are logistic models, neural networks, the Bayesian belief network, and decision trees, all of which provide primary solutions to the problems inherent in the detection and classification of fraudulent data. This paper also addresses the gaps between FFD and the needs of the industry to encourage additional research on neglected topics, and concludes with several suggestions for further FFD research.

917 citations

Book
01 Aug 1996
TL;DR: Fuzzy sets as mentioned in this paper are a class of classes in which there may be grades of membership intermediate between full membership and non-membership, i.e., a fuzzy set is characterized by a membership function which assigns to each object its grade of membership.
Abstract: The notion of fuzziness as defined in this paper relates to situations in which the source of imprecision is not a random variable or a stochastic process, but rather a class or classes which do not possess sharply defined boundaries, e.g., the “class of bald men,” or the “class of numbers which are much greater than 10,” or the “class of adaptive systems,” etc. A basic concept which makes it possible to treat fuzziness in a quantitative manner is that of a fuzzy set, that is, a class in which there may be grades of membership intermediate between full membership and non-membership. Thus, a fuzzy set is characterized by a membership function which assigns to each object its grade of membership (a number lying between 0 and 1) in the fuzzy set. After a review of some of the relevant properties of fuzzy sets, the notions of a fuzzy system and a fuzzy class of systems are introduced and briefly analyzed. The paper closes with a section dealing with optimization under fuzzy constraints in which an approach to...

885 citations