Fuzzy Data

3 Abstract: The overlay functions in GIS are well known and often needed tools for integrating different factors and generating useful information for decision makers. Both the integration model and the generated information usually rely on crisp set theory. In many cases, either the boundaries of classes are not clearly defined, or the classification of a feature into a class

Fuzzy Proximal Support Vector Classification Via Generalized Eigenvalues Jayadeva1, Reshma Khemchandani2, and Suresh Chandra2 1 Department of Electrical Engineering 2 Department of Mathematics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110 016, ...

Although the model-theoretic semantics of the languages used in the Semantic Web are crisps, the need arise to extend them to represent fuzzy data, in the same way fuzzy logic extend first-orderlogic. We will define a fuzzy counterpart of the RDF Model Theory for RDF (section 2) and RDF Schema (section 3). Last, we show how to implement the extended semantics in inference rules (section 4).

A multi-criteria maintenance job scheduling model that minimises equipment and personnel idle times, and lateness of jobs under stochastic-fuzzy uncertainties is presented using a weighted integer linear programming. Job parameters were specified by fuzzy numbers and modelled using triangular membership function representations. The centre of gravity (COG) deffuzification scheme was used within a finite interval to obtain fuzzy variables. The

Abstract: The overlay functions in GIS are well known and often needed tools for integrating different factors and generating useful information for decision makers. Both the integration model and the generated information usually rely on crisp set theory. In many cases, either the boundaries of classes are not clearly defined, or the classification of a feature into a class is not obvious. In such cases, overlay based on fuzzy set and fuzzy logic is unavoidable. The aim of this study is to develop overlay functions on the basis of fuzzy logic and to examine the usability of such functions in integrating data related to indeterminate aspects of a phenomenon. To implement and examine the idea, an application program is developed using VBA programming language and the available Arc Object library. Using this application, a user can generate fuzzy data and use different fuzzy overlay functions to integrate those data. To test the applicability of the model and application, the suitable...

Describes i) necessity of fuzzy DataBase ii) techniques used in the storage and retrieval of fuzzy data in a DataBase or information retrieval system iii) DataBase framework for fuzzy DataBase and iv) the advantages of using object-oriented DataBase framework in fuzzy DataBase. A prototype of fuzzy object-oriented DataBase system, (FOODS), has been developed to demonstrate the feasibility of fuzzy object-oriented

A multi-criteria maintenance job scheduling model that minimises equipment and personnel idle times, and lateness of jobs under stochastic-fuzzy uncertainties is presented using a weighted integer linear programming. Job parameters were specified by fuzzy numbers and modelled using triangular membership function representations. The centre of gravity (COG) deffuzification scheme was used within a finite interval to obtain fuzzy variables. The fuzzy variables were then randomised using the instantaneous probability characteristics of arrival time, processing time and due time of the job specified by probability mass function (PMF). This was used to determine the stochastic measures. The stochastic-fuzzy data then became the model input. The mathematical model constrained by the available equipment, manpower and job availability times within the planning horizon was tested with a 15-job, 24-hour problem with declared equipment and manpower availability levels. The results, analyses an...

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Recently, the fuzzy and the object concepts have been very popular and used in a variety of applications, especially for complex data description. This paper thus proposes a new fuzzy data-mining algorithm for extracting interesting knowledge from quantitative transactions stored as object

Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients' changes are analysed. Researches of skewness and kurtosis coefficients values' changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investig...

Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data in real-world applications, however, usually consists of quantitative values. In the last years, the fuzzy set theory has been applied to data mining for finding interesting association rules in quantitative transactions. Recently, a new rule representation model was presented to perform a genetic lateral tuning of membership functions. It is based on ...

Learning a fuzzy classifier from data is a well-known technique in fuzzy data analysis and many learning algorithms have been proposed, typically in the area of neuro-fuzzy systems. All learning algorithms require a number of parameters to be set by the user. These are typically initial fuzzy partitions for all variables and sometimes also the number of fuzzy rules. Especially, for neuro-fuzzy algorithms the initial choice of parameters can be crucial and if ill-chosen may lead to failure of the learning algorithm. Recent trends in data analysis show that automation is an important issue because it helps to provide advanced analytics to users who are no data analysis experts. In order to fully automate a learning algorithm for fuzzy classifiers we preferably need an algorithm that can determine a suitable initial fuzzy partition for the learning algorithm to start with. In this paper we propose such an algorithm that we have implemented to extend the neuro-fuzzy NEFCLASS. NEFCLASS h...

This paper describes a framework for implementing intrusion detection systems using fuzzy logic. A fuzzy data-mining algorithm is used to extract fuzzy rules for the inference engine. The modular architecture is implemented using the Java expert system shell (Jess) and the FuzzyJess toolkit developed by Sandia National Laboratories and the National Research Council of Canada respectively. Experimental results for a

In marketing, qualitative data are used in theory development to investigate marketing phenomena in more depth. After qualitative data are collected, the judgment-based classification of items into categories is routinely used to summarize and communicate the information contained in the data. In this article, the authors provide marketing researchers with a method that (1) provides useful substantive information about the proportion and degree to which items belong to several categories and (2) measures the classification accuracy of the judges. The model is called the fuzzy latent class model (FLCM), because it extends Dillon and Mulani's (1984) latent class model by freeing it from the restrictive assumption that all items are crisp for a given categorization. Instead, FLCM allows for items to be either crisp or fuzzy. Crisp items belong exclusively to one category, whereas fuzzy items belong—in varying degree—to multiple categories. This relaxation in the assumption about th...

Describes i) necessity of fuzzy DataBase ii) techniques used in the storage and retrieval of fuzzy data in a DataBase or information retrieval system iii) DataBase framework for fuzzy DataBase and iv) the advantages of using object-oriented DataBase framework in fuzzy DataBase. A prototype of fuzzy object-oriented DataBase system, (FOODS), has been developed to demonstrate the feasibility of fuzzy object-oriented DataBase system.

Abstract: The overlay functions in GIS are well known and often needed tools for integrating different factors and generating useful information for decision makers. Both the integration model and the generated information usually rely on crisp set theory. In many cases, either the boundaries of classes are not clearly defined, or the classification of a feature into a class is not obvious. In such cases, overlay based on fuzzy set and fuzzy logic is unavoidable. The aim of this study is to develop overlay functions on the basis of fuzzy logic and to examine the usability of such functions in integrating data related to indeterminate aspects of a phenomenon. To implement and examine the idea, an application program is developed using VBA programming language and the available Arc Object library. Using this application, a user can generate fuzzy data and use different fuzzy overlay functions to integrate those data. To test the applicability of the model and application, the suitable...

A multi-criteria maintenance job scheduling model that minimises equipment and personnel idle times, and lateness of jobs under stochastic-fuzzy uncertainties is presented using a weighted integer linear programming. Job parameters were specified by fuzzy numbers and modelled using triangular membership function representations. The centre of gravity (COG) deffuzification scheme was used within a finite interval to obtain fuzzy variables. The fuzzy variables were then randomised using the instantaneous probability characteristics of arrival time, processing time and due time of the job specified by probability mass function (PMF). This was used to determine the stochastic measures. The stochastic-fuzzy data then became the model input. The mathematical model constrained by the available equipment, manpower and job availability times within the planning horizon was tested with a 15-job, 24-hour problem with declared equipment and manpower availability levels. The results, analyses an...

Fuzzy linear regression is an active area of research. In the literature, fuzziness is considered in outputs and/or in inputs. This paper focuses on both fuzzy inputs and fuzzy outputs. First, some approximations for multiplication of two triangular fuzzy numbers are introduced. Then, to evaluate the fuzzy linear regression, the best approximation is selected to minimize a suitable function via

ABSTRACT The paper proposes a new fuzzy data-mining algorithm for extracting interesting knowledge from quantitative transactions stored as object data. Each item itself is thought of as a class, and each item purchased in a transaction is thought of as an instance. Instances with the same class (item name) may have different quantitative attribute values since they may appear in different transactions. The proposed fuzzy algorithm can be divided into two main phases. The first phase is called the fuzzy intra-object mining phase, in which the linguistic large itemsets associated with the same classes (items) but with different attributes are derived. The second phase is called the fuzzy inter-object mining phase, in which the large itemsets are derived and used to represent the relationship among different kinds of objects. Experimental results also show the effects of the proposed algorithm.

Starting from fuzzy binary data represented as tables in the fuzzy relational database, in this paper, we use fuzzy formal concept analysis to reduce the tables size to only keep the minimal rows in each table, without losing knowledge (i.e., association rules extracted from reduced databases are identical at given precision level). More specifically, we develop a fuzzy extension of

We introduce a fuzzy set theoretic approach for dealing with uncertainty in images in the context of spatial and topological relations existing among the objects in the image. We propose an object-oriented graph theoretic model for representing an image and this model allows us to assess the similarity between images using the concept of (fuzzy) graph matching. Sufficient flexibility has been provided in the similarity algorithm so that different features of an image may be independently focused upon.