99,281 research outputs found
Matroidal structure of generalized rough sets based on symmetric and transitive relations
Rough sets are efficient for data pre-process in data mining. Lower and upper
approximations are two core concepts of rough sets. This paper studies
generalized rough sets based on symmetric and transitive relations from the
operator-oriented view by matroidal approaches. We firstly construct a
matroidal structure of generalized rough sets based on symmetric and transitive
relations, and provide an approach to study the matroid induced by a symmetric
and transitive relation. Secondly, this paper establishes a close relationship
between matroids and generalized rough sets. Approximation quality and
roughness of generalized rough sets can be computed by the circuit of matroid
theory. At last, a symmetric and transitive relation can be constructed by a
matroid with some special properties.Comment: 5 page
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 sets and matroidal contraction
Rough sets are efficient for data pre-processing in data mining. As a
generalization of the linear independence in vector spaces, matroids provide
well-established platforms for greedy algorithms. In this paper, we apply rough
sets to matroids and study the contraction of the dual of the corresponding
matroid. First, for an equivalence relation on a universe, a matroidal
structure of the rough set is established through the lower approximation
operator. Second, the dual of the matroid and its properties such as
independent sets, bases and rank function are investigated. Finally, the
relationships between the contraction of the dual matroid to the complement of
a single point set and the contraction of the dual matroid to the complement of
the equivalence class of this point are studied.Comment: 11 page
A Data Preprocessing Algorithm for Classification Model Based On Rough Sets
AbstractAimed to solve the limitation of abundant data to constructing classification modeling in data mining, the paper proposed a novel effective preprocessing algorithm based on rough sets. Firstly, we construct the relation Information System using original data sets. Secondly, make use of attribute reduction theory of Rough sets to produce the Core of Information System. Core is the most important and necessary information which cannot reduce in original Information System. So it can get a same effect as original data sets to data analysis, and can construct classification modeling using it. Thirdly, construct indiscernibility matrix using reduced Information System, and finally, get the classification of original data sets. Compared to existing techniques, the developed algorithm enjoy following advantages: (1) avoiding the abundant data in follow-up data processing, and (2) avoiding large amount of computation in whole data mining process. (3) The results become more effective because of introducing the attributes reducing theory of Rough Sets
Encapsulation of Soft Computing Approaches within Itemset Mining a A Survey
Data Mining discovers patterns and trends by extracting knowledge from large databases. Soft Computing techniques such as fuzzy logic, neural networks, genetic algorithms, rough sets, etc. aims to reveal the tolerance for imprecision and uncertainty for achieving tractability, robustness and low-cost solutions. Fuzzy Logic and Rough sets are suitable for handling different types of uncertainty. Neural networks provide good learning and generalization. Genetic algorithms provide efficient search algorithms for selecting a model, from mixed media data. Data mining refers to information extraction while soft computing is used for information processing. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Association rule mining (ARM) and Itemset mining focus on finding most frequent item sets and corresponding association rules, extracting rare itemsets including temporal and fuzzy concepts in discovered patterns. This survey paper explores the usage of soft computing approaches in itemset utility mining
Rough sets for mining educational data
While educational data are typically analyzed with statistical software, data mining techniques are increasingly appropriate in revealing complex relationships among multiple variables in large amounts of data. We experimented with the rough set method in conjunction with statistical analysis to identify patterns in, and thereby extract meaning from, complex educational data. Results establish the benefits of combining rough set decision making with stochastic analysis in mining exceedingly complex and difficult to interpret educational data sets
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