Catálogo de publicaciones - libros

In this paper we study the impact of selecting different entropy measures in the framework of intuitionistic fuzzy image processing and especially in the process of intuitionistic fuzzification of images. Different notions of entropy characterized by different properties are reviewed and their behavior is thoroughly studied under the scope of performing contrast enhancement. Finally, experimental results using gray-scale images reveal the characteristics of the aforementioned measures.

Intuitionistic fuzzy special sets is a special case of intuitionistic fuzzy sets. In this paper, under the framework of information systems, the relationship between intuitionistic fuzzy special sets and rough sets is analyzed. Based on basic intuitionistic fuzzy special sets of information systems, intuitionistic fuzzy special -algebra are generated, and rough sets are embedded in the intuitionistic fuzzy special -algebra. Naturally, distances (.., Hamming distance or Euclidean distance) of intuitionistic fuzzy special sets in intuitionistic fuzzy special -algebra can be used to evaluate predication rules of information systems which is an important subject of rough set theory.

We address a novel issue for logic programming, namely the problem of evaluating ranked top-k queries. The problem occurs for instance, when we allow queries such as “find cheap hotels close to the conference location” in which vague predicates like cheap and close occur. Vague predicates have the effect that each tuple in the answer set has now a score in [0,1]. We show how to compute the top-k answers in case the set of facts is huge, without evaluating all the tuples.

This paper presents a conceptual querying approach with reference to a document collection where the target is the concepts appearing in documents, rather than the documents in the collection. An ontology formalism and a special notion of ”instantiated ontology” are introduced. The latter is a structure reflecting the content in the document collection in that; it is a restriction of a general world knowledge ontology to the concepts instantiated in the collection. The notion of ontology-based similarity is briefly described and a language for navigation and retrieval of concepts in the ontology is presented.

Tree mining consists in discovering the frequent subtrees from a forest of trees. This problem has many application areas. For instance, a huge volume of data available from the Internet is now described by trees (e.g. XML). Still, for several documents dealing with the same topic, this description is not always the same. It is thus necessary to mine a common structure in order to query these documents. Biology is another field where data may be described by means of trees. The problem of mining trees has now been addressed for several years, leading to well-known algorithms. However, these algorithms can hardly deal with real data in a soft manner. Indeed, they consider a subtree as in the super-tree. This means that all the nodes must appear. In this paper, we extend this definition to fuzzy inclusion based on the idea that a tree is included to a certain degree within another one, this fuzzy degree being correlated to the number of .

This paper concerns the modelling of fuzzy information in geographic databases. In the past, a theoretical model for fuzzy regions has been presented, along with various operations useful in a geographic database: union, intersection, topology, bounding rectangle, etc. and feasible models for implementation based on this theoretical model. Now, the attention is directed at some of the problems that can occur when determining numerical properties of fuzzy regions: what type of result is expected (and desired), and how does this impact the definitions of the operations. As an example, the definition of the surface area of a fuzzy set is studied in more detail.

In many problems the information can be imprecise and uncertain simultaneously. Linguistic terms can be then used to represent each one of these aspects. In some applications it is desirable to combine imprecision and uncertainty into a single value which appropriately describes the original information. We propose a method to combine imprecision and uncertainty when they are expressed as trapezoidal fuzzy numbers and the final goal is to obtain a normalized fuzzy number. This property is very useful in several applications like flexible querying processes, where the linguistic label used in the query is always normalized.

In this paper, weighted aggregation of extended possibilistic truth values will be presented. The impact of the weights on both conjunctive and disjunctive aggregation will be handled, and special care will be given to the case where both conjunctive and disjunctive aggregation are mixed together. The problems that can arise will be illustrated and solutions will be proposed.

In [1,2] we studied various concepts of bipolar queries (cf. Dubois and Prade [3]). We advocated the use of a fuzzified version of the original crisp definition by Lacroix and Lavency [4]. However, the general fuzzification proposed leaves open the choice of a representation of logical connectives and quantifiers. In the present paper we study the influence of the choice some representations that are popular in fuzzy logic on matching degrees of the tuples and their resulting ordering.

Comparing objects is a very general problem domain. The way objects are compared can differ a lot, in the way objects are related to each other as well as in the meaning that is attributed to the result of a comparison. In this paper we develop a theoretical framework for comparing objects. This framework can be tailored to the specific needs of an application through the choice of appropriate operators and evaluation domain (i.e. the domain in which the comparison results are expressed).