Pawlak put forward professional book which exposited fully and systematically rough sets theory and laid strict math base. The standard model of rough sets becomes a special case of vpmodel. Chapter 2 rough sets and reasoning from data presents the application of rough set concept to reason from data data mining. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Frequent sequential patterns mining finds intresting event sequences from various real world databases under study. Pawlak, rough sets theoretical aspect of reasoning about data, kluweracademic pubilishers1991.
What is known about rs in computer science, a rough set, first described by a polish computer scientist zdzislaw pawlak, is a formal approximation of a crisp set i. Obviously rough sets, in contrast to precise sets, cannot be characterized in terms of information about their elements. We study multigranulation spaces of two equivalences. Selected methods and applications in management and engineering provides context to rough set theory, with each chapter exploring a realworld application of rough sets. Rough sets and applications rough set theory is a way of representing and reasoning imprecision and uncertain information in data.
Rough sets in this work we base our approach on the rough sets originally proposed in 7 and further investigated by several scholars. An nphard problem is defined as a problem that cannot be solved in. With any rough set a pair of precise sets, called the lower and the upper approximation of the rough set, is associated. Nagamachi at hiroshima university about 30 years ago and has spread throughout the world. Pdf rough sets clustering and markov model for web access. Developing a bankruptcy prediction model via rough sets. Chapter 5 rough sets and conflict analysis discuses the application of rough set concept to study conflict. The rough sets basic model created by pawlak is a model with a simple and solid mathematical basis. Chapter 3 rough sets and bayes theorem gives a new look on bayes theorem and shows that bayes rule can be used differently to that offered by classical bayesian reasoning. Hybrid rough sets and applications in uncertain decision. Pdf rough set model using lattice theory ravi shankar.
Pdf rough sets clustering and markov model for web. This article examines basic issues of data mining using the theory of rough sets, which is a recent proposal for generalizing classical set theory. Rough set theory has been a methodology of database mining or knowledge discovery in relational databases. In proceedings of iea 2006, kansei engineering session, maastricht, 2006, pp. Modeling intelligent system in the field of medical diagnosis is still a challenging work. We investigate in this paper approximate operations on sets, approximate equality of sets, and approximate inclusion of sets. Rough set theory, data mining, decision table, decision rule, data representation. Rough set theory, introduced by zdzislaw pawlak in the early 1980s 11, 12, is a new mathematical tool to deal with vagueness and uncertainty.
In section3, we propose a rough set model in generalized singlevalued neutrosophic approximation spaces. Finally, some description about applications of the data mining system with rough set theory is included. Recent research has shown that a generalized rough set model need not be based on equivalence relation axioms. Kansei engineering and application of the rough sets model. This book is designed for researchers and students working in the field of fuzzy sets, rough sets, soft sets, and their hybrid models. A variable precision coveringbased rough set model based. Comprehensive evaluation model of building energy efficiency. The notion of rough sets was introduced by z pawlak in his seminal paper of 1982 pawlak 1982. The first is a method based on rough sets theory and the second is the location model method from the field of discriminant analysis. Section4gives two extended models and studies some related properties.
Since introduction of the theory of rough set in early eighties, considerable work has been done on the development and application of this new theory. In particular we look at his motivations for introducinginformation storage and retrieval systems and how this, eventually, led to rough sets theory. Rough sets mathematical foundations lech polkowski. Decisiontheoretic rough set models, rough sets and knowledge technology, second international conference, rskt 2007, proceedings, lnai 4481, pp. Rough sets can identify and characterize the uncertain systems. An analysis and comparison of kansei engineering data applied to ecological river landscape design in terms of a statistical analysis and rough sets model. This approach seems to be of fundamental importance t.
Although the field remained active through the 90s and until today, this book is still essential for anyone wanting exposure to the various aspects and. This theory is especially well suited to deal with inconsistencies in the process of knowledge acquisition. In the mathematical theory of decisions, decisiontheoretic rough sets dtrs is a probabilistic extension of rough set classification. Rough sets clustering and markov model for web access prediction. In this model, a solution to an algebraic structure problem is first provided in an information system. Introduction rough set theory was developed by zdzislaw pawlak in the early 1980s. In the literature of rough sets, hierarchical classifiers have been studied by many authors. A rapid growth of interest in rough set theory 290 and its applications can be lately seen in the number of international workshops, conferences and seminars that are either directly dedicated to rough sets, include the subject in their programs, or simply accept papers that use this approach to. The book is a tutorial overview written by the originator of rough set theory of the work which has been done in this field by himself and many others during the decade of the 80s. Rough sets is relevant to managers striving to improve their businesses, industry researchers looking to improve the efficiency of their solutions, and university. Pdf kansei engineering and application of the rough sets. The rough sets theory is able to deal with any kind of data, irrespective of linear or nonlinear characteristics of the data.
How to deal with common problems in rough set theory based on rough concepts is. Intelligent systems in medical diagnosis can be utilized as a. Pawlak, is a formal approximation of a crisp set i. However, in the existing roughset model the probabilistic information crucial to nondeterministic classification recognition problems is not taken into consideration. A model based on rough sets for situation comprehension. Rst is an elegant theory when applied to small data sets because it can always find the minimal reduct and generate minimal rule sets. Rough set theory has been successfully applied to many fields, such as data mining, pattern recognition, and machine learning. Logistic regression was proposed in the early 19 th century for the description of the growth of population and it became frequently available in statistical packages in the early 1980s cramer, 2003. Pdf the structure of multigranular rough sets semantic.
Keeping the complicated mathematics to a minimum, hybrid rough sets and applications in uncertain decisionmaking provides a systematic introduction to the methods and application of the hybridization for rough set theory with other related soft technology theories, including probability, grey systems, fuzzy sets, and artificial neural networks. The pawlak rough set model is based on the concept of an equivalence relation. Originated from the simple information model, the basic idea of the rough set theory can be divided into two parts. A rough set is a formal approximation of a crisp set in terms of a pair of sets that give the lower and upper approximation of the original set learn more in. This paper focuses on models of generalized fuzzy rough sets, a generalized model of fuzzy rough sets based on general fuzzy relations are studied, properties and algebraic characterization of the model are revealed, and relationships between this model and related. However, the general solution for finding the minimal reduct is nphard 78. In this paper, we give a measurement of importance degree of attributes based on knowledge discovery in the decision information system, which satisfies the conditions of fuzzy measure. Data mining using extensions of the rough set model lingras. This part attempts to introduce rough set theory rst and its application to data analysis. Threeway decisions with probabilistic rough sets sciencedirect. Reduct and core are the two important concepts in rough sets theory. Rough sets may also be described by using the notion of rough membership functions, which will be discussed in section 3. The latticetheoretical properties of socalled optimistic and pessimistic multigranular approximation systems are given. Variable precision rough set model binary relation based rough sets.
An evaluation model based on rough sets scientific. In the presented approach, inconsistencies are not corrected. Like rough sets, the lower and upper approximations of a set are used. However, most of real world databases are subject to updations with advancement of time. We also consider the ordered sets of rough sets determined by these approximation pairs. In multiattribute decision making, it is critical to indentify the importance degree of attributes before the overall assessment of the alternatives. The third part of the presentation applications of rough set theory to solve some students enrollment problems in the workshop for the project analysis, d. The concept of rough, or approximation, set s was introduced by pawlak, and is based on the single assumption that information is associated with. In the standard version of rough set theory pawlak 1991, the lower and upper. The primary advantage of a vpmodel, in the context of data analysis applications, is its ability to recognize the presence of data dependencies in situations where data items are considered independent by the original rough sets model. It is a formal theory derived from fundamental research on logical properties of information systems. Coveringbased rough sets are proposed to generalize this theory for dealing with covering data. In classical rough set theory, it is not possible to consider realvalued or noisy data. And many rough set models based on two universes have been developed from different points of view.
In the standard version of rough set theory pawlak 1991, the lower and upperapproximation sets are crisp sets, but in other. It introduces the concept and basic properties of generalized intervalvalued intuitionistic fuzzy soft sets. One of the basic assumptions of simple regression model is that the dependent variable is quantitative whereas the independent variable may be either quantitative or qualitative in. By introducing a concept of misclassification rate functions, an extended variable precision coveringbased rough set model is proposed in. Applications, case studies and software systems physicaverlag, wurzbury, 1998. Additionally, the rough set approach to lower and upper approximations and certain possible rule sets concepts are introduced. Application of rough set based models in medical diagnosis. The model that is used in this study follows the knowledge discovery in database process given in 1, 3 closely. The key issue is to compute lower and upper approxima. Rough set theory was developed by zdzislaw pawlak in the early 1980s. Finally it provides a profound reference to the area of mathematical foundations of rough set theory.
Rough atanassovs intuitionistic fuzzy sets model over two. The presented approach may be considered as an alternative to fuzzy sets theory and tolerance theory. According to the knowledge discovery process based on rough sets and combining the research target, we design a type of college students network research system model based on rough sets as shown in figure 1. The integration of threeway decisionmaking and hierarchical classifiers may produce fruitful results. Various real lifeapplications of rough set theory have shown its usefulness in many domains. Emergent model based on hybrid rough sets systems 237 u sex clinical stage infection p1 f t yes p2 m t yes p3 f b no p4 m t yes p5 m t yes p6 m t no p7 f t yes p8 f b no p9 m b no p10 m t no table 1. The kernel rough sets model, which has fuzziness, is susceptible to noise in the decision system. The package roughsets attempts to provide a complete tool to model and analyze information systems based on rough set theory rst and fuzzy rough set theory frst. In computer science, a rough set, first described by polish computer scientist zdzislaw i. The neighborhood rough sets model can handle noisy data well.
The lower approximation consists of all objects which surely belong to the set and the. In particular, we leverage on the results of yao, e. The standard model of rough sets becomes a special case of vp model. Kernel rough sets and neighborhood rough sets are two important models that differ in terms of granulation. While the classical rst proposed by pawlak in 1982 is explained in detail in this section, some recent advancements will be treated in the documentation of the related functions.
The rough sets rs rationale consists of approximating a set using a pair of sets, termed lower and upper approximations. Papers please contact one of us for copies dgpapers on rough sets and related stuff books. Sets, fuzzy sets and rough sets warsaw university of. Very promising new areas of application of the rough set concept seems to. A model based on rough sets for situation comprehension and. Kernel neighborhood rough sets model and its application. S s symmetry article a novel rough set model in generalized single valued neutrosophic approximation spaces and its application zhilian guo 1, yanling liu 2 and hailong yang 2, 1 college of economics, northwest university of political science and law, xian 710063, china. Yiyu yao, the extension makes use of loss functions to derive and region parameters. Pawlak, rough sets, international journal of computer and information sciences, vol. System model of college students network behavior research. In this paper, a novel model, that is, rough atanassovs intuitionistic fuzzy sets model over two different universes, is firstly proposed from atanassovs intuitionistic point of view. Within the classical rough set model, existing studies on rules are unnecessarily restricted. Rough sets theory was brought forward by pawlak z from poland in 1982.
This thesis proposes and develops an approach based on fuzzy rough sets, fuzzy rough feature selection frfs, that. The results based on statistical analysis and rough sets theory are. The consideration of approximation problem of fuzzy sets in fuzzy information systems results in theory of fuzzy rough sets. Application of the variable precision rough sets model to. Pdf kansei engineering and application of the rough sets model. Because the rough sets show the information as easy to understand logic patterns, where the inspection and validity of the data required or the decisions are taken by the rules and suitable for the supported situations, this method is successful binay, 2002. Bayesian decision theory, decisiontheoretic rough sets, probabilistic rough sets, variable precision rough sets 1 introduction ever since the introduction of rough set theory by pawlak in 1982 10,11, many proposals have been made to incorporate probabilistic approaches into the theory 14,2931. Rough sets doesnt need any transcendental information except data sets about problems required, and it is the most major difference from the theory. Proposed by professor pawlak in 1982, the rough set theory is an important mathematical tool to deal with imprecise, inconsistent, incomplete information and knowledge. Pawlak, rough sets theoretical aspect of reasoning about data, kluwer academic pubilishers 1991. In the standard version of rough set theory pawlak 1991, the lower and. The primary advantage of a vp model, in the context of data analysis applications, is its ability to recognize the presence of data dependencies in situations where data items are considered independent by the original rough sets model. This paper proposes a rough set model based on formal concept analysis. A survey on rough set theory and its applications sciencedirect.
Combining rough and fuzzy sets for feature selection. However, the main limitation of rough setbased feature selection in the literature is the restrictive requirement that all data is discrete. A novel rough set model in generalized single valued. A rapid growth of interest in rough set theory 290 and its applications can be lately seen in the number of international workshops, conferences and seminars that are either directly dedicated to rough sets, include the subject in their programs, or simply accept papers that use this approach to solve problems at hand. Data mining using extensions of the rough set model. Decision making, data mining, knowledge representation, university of milanobicocca, milan, italy, 2529 july, 2016 introduction to rough sets. Pdf a rough sets partitioning model for incremental mining. This booklet is a modified version of lectures delivered at the tarragona university seminar on formal languages and rough sets in august 2003.
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