4,059 research outputs found
Connectedness of graphs and its application to connected matroids through covering-based rough sets
Graph theoretical ideas are highly utilized by computer science fields
especially data mining. In this field, a data structure can be designed in the
form of tree. Covering is a widely used form of data representation in data
mining and covering-based rough sets provide a systematic approach to this type
of representation. In this paper, we study the connectedness of graphs through
covering-based rough sets and apply it to connected matroids. First, we present
an approach to inducing a covering by a graph, and then study the connectedness
of the graph from the viewpoint of the covering approximation operators.
Second, we construct a graph from a matroid, and find the matroid and the graph
have the same connectedness, which makes us to use covering-based rough sets to
study connected matroids. In summary, this paper provides a new approach to
studying graph theory and matroid theory
Rough matroids based on coverings
The introduction of covering-based rough sets has made a substantial
contribution to the classical rough sets. However, many vital problems in rough
sets, including attribution reduction, are NP-hard and therefore the algorithms
for solving them are usually greedy. Matroid, as a generalization of linear
independence in vector spaces, it has a variety of applications in many fields
such as algorithm design and combinatorial optimization. An excellent
introduction to the topic of rough matroids is due to Zhu and Wang. On the
basis of their work, we study the rough matroids based on coverings in this
paper. First, we investigate some properties of the definable sets with respect
to a covering. Specifically, it is interesting that the set of all definable
sets with respect to a covering, equipped with the binary relation of inclusion
, constructs a lattice. Second, we propose the rough matroids based
on coverings, which are a generalization of the rough matroids based on
relations. Finally, some properties of rough matroids based on coverings are
explored. Moreover, an equivalent formulation of rough matroids based on
coverings is presented. These interesting and important results exhibit many
potential connections between rough sets and matroids.Comment: 15page
Geometric lattice structure of covering and its application to attribute reduction through matroids
The reduction of covering decision systems is an important problem in data
mining, and covering-based rough sets serve as an efficient technique to
process the problem. Geometric lattices have been widely used in many fields,
especially greedy algorithm design which plays an important role in the
reduction problems. Therefore, it is meaningful to combine coverings with
geometric lattices to solve the optimization problems. In this paper, we obtain
geometric lattices from coverings through matroids and then apply them to the
issue of attribute reduction. First, a geometric lattice structure of a
covering is constructed through transversal matroids. Then its atoms are
studied and used to describe the lattice. Second, considering that all the
closed sets of a finite matroid form a geometric lattice, we propose a
dependence space through matroids and study the attribute reduction issues of
the space, which realizes the application of geometric lattices to attribute
reduction. Furthermore, a special type of information system is taken as an
example to illustrate the application. In a word, this work points out an
interesting view, namely, geometric lattice to study the attribute reduction
issues of information systems
Matroidal approaches to rough sets via closure operators
AbstractThis paper studies rough sets from the operator-oriented view by matroidal approaches. We firstly investigate some kinds of closure operators and conclude that the Pawlak upper approximation operator is just a topological and matroidal closure operator. Then we characterize the Pawlak upper approximation operator in terms of the closure operator in Pawlak matroids, which are first defined in this paper, and are generalized to fundamental matroids when partitions are generalized to coverings. A new covering-based rough set model is then proposed based on fundamental matroids and properties of this model are studied. Lastly, we refer to the abstract approximation space, whose original definition is modified to get a one-to-one correspondence between closure systems (operators) and concrete models of abstract approximation spaces. We finally examine the relations of four kinds of abstract approximation spaces, which correspond exactly to the relations of closure systems
Conference Program
Document provides a list of the sessions, speakers, workshops, and committees of the 32nd Summer Conference on Topology and Its Applications
Geometric lattice structure of covering-based rough sets through matroids
Covering-based rough set theory is a useful tool to deal with inexact,
uncertain or vague knowledge in information systems. Geometric lattice has
widely used in diverse fields, especially search algorithm design which plays
important role in covering reductions. In this paper, we construct four
geometric lattice structures of covering-based rough sets through matroids, and
compare their relationships. First, a geometric lattice structure of
covering-based rough sets is established through the transversal matroid
induced by the covering, and its characteristics including atoms, modular
elements and modular pairs are studied. We also construct a one-to-one
correspondence between this type of geometric lattices and transversal matroids
in the context of covering-based rough sets. Second, sufficient and necessary
conditions for three types of covering upper approximation operators to be
closure operators of matroids are presented. We exhibit three types of matroids
through closure axioms, and then obtain three geometric lattice structures of
covering-based rough sets. Third, these four geometric lattice structures are
compared. Some core concepts such as reducible elements in covering-based rough
sets are investigated with geometric lattices. In a word, this work points out
an interesting view, namely geometric lattice, to study covering-based rough
sets
Algorithmic statistics revisited
The mission of statistics is to provide adequate statistical hypotheses
(models) for observed data. But what is an "adequate" model? To answer this
question, one needs to use the notions of algorithmic information theory. It
turns out that for every data string one can naturally define
"stochasticity profile", a curve that represents a trade-off between complexity
of a model and its adequacy. This curve has four different equivalent
definitions in terms of (1)~randomness deficiency, (2)~minimal description
length, (3)~position in the lists of simple strings and (4)~Kolmogorov
complexity with decompression time bounded by busy beaver function. We present
a survey of the corresponding definitions and results relating them to each
other
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