10,620 research outputs found
Characteristic of partition-circuit matroid through approximation number
Rough set theory is a useful tool to deal with uncertain, granular and
incomplete knowledge in information systems. And it is based on equivalence
relations or partitions. Matroid theory is a structure that generalizes linear
independence in vector spaces, and has a variety of applications in many
fields. In this paper, we propose a new type of matroids, namely,
partition-circuit matroids, which are induced by partitions. Firstly, a
partition satisfies circuit axioms in matroid theory, then it can induce a
matroid which is called a partition-circuit matroid. A partition and an
equivalence relation on the same universe are one-to-one corresponding, then
some characteristics of partition-circuit matroids are studied through rough
sets. Secondly, similar to the upper approximation number which is proposed by
Wang and Zhu, we define the lower approximation number. Some characteristics of
partition-circuit matroids and the dual matroids of them are investigated
through the lower approximation number and the upper approximation number.Comment: 12 page
Parametric matroid of rough set
Rough set is mainly concerned with the approximations of objects through an
equivalence relation on a universe. Matroid is a combinatorial generalization
of linear independence in vector spaces. In this paper, we define a parametric
set family, with any subset of a universe as its parameter, to connect rough
sets and matroids. On the one hand, for a universe and an equivalence relation
on the universe, a parametric set family is defined through the lower
approximation operator. This parametric set family is proved to satisfy the
independent set axiom of matroids, therefore it can generate a matroid, called
a parametric matroid of the rough set. Three equivalent representations of the
parametric set family are obtained. Moreover, the parametric matroid of the
rough set is proved to be the direct sum of a partition-circuit matroid and a
free matroid. On the other hand, since partition-circuit matroids were well
studied through the lower approximation number, we use it to investigate the
parametric matroid of the rough set. Several characteristics of the parametric
matroid of the rough set, such as independent sets, bases, circuits, the rank
function and the closure operator, are expressed by the lower approximation
number.Comment: 15 page
Knowledge Engineering from Data Perspective: Granular Computing Approach
The concept of rough set theory is a mathematical approach to uncertainly and vagueness in data analysis, introduced by Zdzislaw Pawlak in 1980s. Rough set theory assumes the underlying structure of knowledge is a partition. We have extended Pawlak’s concept of knowledge to coverings. We have taken a soft approach regarding any generalized subset as a basic knowledge. We regard a covering as basic knowledge from which the theory of knowledge approximations and learning, knowledge dependency and reduct are developed
Covering matroid
In this paper, we propose a new type of matroids, namely covering matroids,
and investigate the connections with the second type of covering-based rough
sets and some existing special matroids. Firstly, as an extension of
partitions, coverings are more natural combinatorial objects and can sometimes
be more efficient to deal with problems in the real world. Through extending
partitions to coverings, we propose a new type of matroids called covering
matroids and prove them to be an extension of partition matroids. Secondly,
since some researchers have successfully applied partition matroids to
classical rough sets, we study the relationships between covering matroids and
covering-based rough sets which are an extension of classical rough sets.
Thirdly, in matroid theory, there are many special matroids, such as
transversal matroids, partition matroids, 2-circuit matroid and
partition-circuit matroids. The relationships among several special matroids
and covering matroids are studied.Comment: 15 page
Information Flow Model for Commercial Security
Information flow in Discretionary Access Control (DAC) is a well-known difficult problem. This paper formalizes the fundamental concepts and establishes a theory of information flow security. A DAC system is information flow secure (IFS), if any data never flows into the hands of owner’s enemies (explicitly denial access list.
Knowledge Engineering in Search Engines
With large amounts of information being exchanged on the Internet, search engines have become the most popular tools for helping users to search and filter this information. However, keyword-based search engines sometimes obtain information, which does not meet user’ needs. Some of them are even irrelevant to what the user queries. When the users get query results, they have to read and organize them by themselves. It is not easy for users to handle information when a search engine returns several million results. This project uses a granular computing approach to find knowledge structures of a search engine. The project focuses on knowledge engineering components of a search engine. Based on the earlier work of Dr. Lin and his former student [1], it represents concepts in the Web by simplicial complexes. We found that to represent simplicial complexes adequately, we only need the maximal simplexes. Therefore, this project focuses on building maximal simplexes. Since it is too costly to analyze all Web pages or documents, the project uses the sampling method to get sampling documents. The project constructs simplexes of documents and uses the simplexes to find maximal simplexes. These maximal simplexes are regarded as primitive concepts that can represent Web pages or documents. The maximal simplexes can be used to build an index of a search engine in the future
Distributed Functional Scalar Quantization Simplified
Distributed functional scalar quantization (DFSQ) theory provides optimality
conditions and predicts performance of data acquisition systems in which a
computation on acquired data is desired. We address two limitations of previous
works: prohibitively expensive decoder design and a restriction to sources with
bounded distributions. We rigorously show that a much simpler decoder has
equivalent asymptotic performance as the conditional expectation estimator
previously explored, thus reducing decoder design complexity. The simpler
decoder has the feature of decoupled communication and computation blocks.
Moreover, we extend the DFSQ framework with the simpler decoder to acquire
sources with infinite-support distributions such as Gaussian or exponential
distributions. Finally, through simulation results we demonstrate that
performance at moderate coding rates is well predicted by the asymptotic
analysis, and we give new insight on the rate of convergence
Automatic generation of fuzzy classification rules using granulation-based adaptive clustering
A central problem of fuzzy modelling is the generation of fuzzy rules that fit the data to the highest possible extent. In this study, we present a method for automatic generation of fuzzy rules from data. The main advantage of the proposed method is its ability to perform data clustering without the requirement of predefining any parameters including number of clusters. The proposed method creates data clusters at different levels of granulation and selects the best clustering results based on some measures. The proposed method involves merging clusters into new clusters that have a coarser granulation. To evaluate performance of the proposed method, three different datasets are used to compare performance of the proposed method to other classifiers: SVM classifier, FCM fuzzy classifier, subtractive clustering fuzzy classifier. Results show that the proposed method has better classification results than other classifiers for all the datasets used
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