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2010 IEEE International Conference on Granular Computing

14-16 Aug. 2010

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Displaying Results 1 - 25 of 185
  • [Front cover]

    Publication Year: 2010, Page(s): C1
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  • [Title page i]

    Publication Year: 2010, Page(s): i
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  • [Title page iii]

    Publication Year: 2010, Page(s): iii
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  • [Copyright notice]

    Publication Year: 2010, Page(s): iv
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  • Table of contents

    Publication Year: 2010, Page(s):v - xiii
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  • Preface

    Publication Year: 2010, Page(s):xiv - xv
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  • Conference organization

    Publication Year: 2010, Page(s):xvi - xvii
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  • Program Committee Members

    Publication Year: 2010, Page(s):xviii - xx
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  • Advances in Rough and Fuzzy Set Information Systems

    Publication Year: 2010, Page(s):3 - 4
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (184 KB) | HTML iconHTML

    Rough sets and fuzzy sets are frameworks for the representation of uncertainty. They have been used in various applications such as the imprecise querying of crisp data, uncertainty management in databases, the mining of spatial data, and measurement of information content. This paper discusses such advances in rough and fuzzy set information systems. View full abstract»

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  • A Summary on Fuzzy Probability Theory

    Publication Year: 2010, Page(s):5 - 6
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (117 KB) | HTML iconHTML

    Fuzzy probability theory is an extension of probability theory to dealing with mixed probabilistic/non-probabilistic uncertainty. It provides a theoretical basis to model uncertainty which is only partly characterized by randomness and defies a pure probabilistic modeling with certainty due to a lack of trustworthiness or precision of the data or a lack of pertinent information. The fuzzy probabil... View full abstract»

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  • A Summary on Granular Computing and Modeling of the Uncertainty in Quantum Mechanics

    Publication Year: 2010, Page(s):7 - 8
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (172 KB) | HTML iconHTML

    Quantum uncertainty is analyzed by means of the non commuting nature of the position operators and its conjugate momentum operator. Q-representations of the quantum state, the wave function, as well as the quantum operators which correspond to the dynamical observables in the measurement are taken as the basic tools to demonstrate the Heisenberg's uncertainty relations. Uncertainty arises from a p... View full abstract»

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  • An Update and an Overview on Philosophical Foundation of Granular Computing

    Publication Year: 2010, Page(s):9 - 10
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (177 KB) | HTML iconHTML

    The objective of this article is to provide a fair review on certain important aspects related to philosophical foundation of granular computing (GrC). We summarize and analyze existing literature to find out what has led to GrC, what have been explicitly stated, what are implied, and what are still lacking (or overlooked). Due to the huge amount of literature in this area and diverse viewpoints o... View full abstract»

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  • Can Fuzzy Logic Bring Complex Environmental Problems into Focus?

    Publication Year: 2010, Page(s):11 - 17
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (268 KB) | HTML iconHTML

    The application of Fuzzy Multi Criteria Decision Making (FMCDM) with case studies for ranking the industries, the paper presents the multifaceted formalism along with Type 1 Fuzzy logic Inference System (Type 1 FLS) for fuzzily describing river water quality for bathing. It is the belief that the Computing With Words (CWW) methodology with focus on fuzzy logic will be of practical utility in addre... View full abstract»

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  • Granular Neural Networks with Decision Fusion

    Publication Year: 2010, Page(s):18 - 19
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (188 KB) | HTML iconHTML

    A granular neural network is designed to deal with granular data fusion and granular knowledge discovery. From a data granulation point of view, the granular neural network can process granular data in a database. From a knowledge discovery point of view, the granular neural network is able to learn internal granular relations between inputs and outputs, and predict new relations. From a decision ... View full abstract»

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  • A Summary and Update of “Granular Computing and Data Mining for Ordered Data: The Dominance-Based Rough Set Approach”

    Publication Year: 2010, Page(s):20 - 21
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (111 KB) | HTML iconHTML

    This article describes Dominance-based Rough Set Approach (DRSA) to granular computing and data mining. DRSA was first introduced by the authors as a generalization of the rough set approach for dealing with multicriteria decision analysis, where preference order is important. The ordering is also important, however, in many other problems of data analysis. Even when the ordering seems absent, the... View full abstract»

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  • Selected Topics of Data Mining

    Publication Year: 2010, Page(s):22 - 23
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (124 KB) | HTML iconHTML

    We briefly summarize handling inconsistent data, discretization of numerical attributes, missing attribute values, and rule induction, with LERS as a typical example of a data mining system. View full abstract»

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  • Principles, Applications, and Trends of Granular Computing

    Publication Year: 2010, Page(s):24 - 25
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (203 KB) | HTML iconHTML

    This paper is to introduce the principles, models and perspectives of granular computing, and discuss its applications in various domains such as data mining, software engineering, and human-computer interaction. The research and development trends in granular computing are predicted. View full abstract»

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  • Genetic-Fuzzy Data Mining Techniques

    Publication Year: 2010, Page(s):26 - 27
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (123 KB) | HTML iconHTML

    In this article, we have introduced some genetic-fuzzy data mining techniques and their classification. The concept of fuzzy sets is used to handle quantitative transactions and the process of genetic calculation is executed to find appropriate membership functions. The main contributions of this paper are that we first divided the genetic-fuzzy mining problems into four kinds according to the typ... View full abstract»

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  • A Summary of Genetic-Fuzzy Data Mining Techniques

    Publication Year: 2010, Page(s):28 - 29
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (222 KB) | HTML iconHTML

    In this article, we have introduced some genetic-fuzzy data mining techniques and their classification. The concept of fuzzy sets is used to handle quantitative transactions and the process of genetic calculation is executed to find appropriate membership functions. The main contributions of this paper are that we first divided the genetic-fuzzy mining problems into four kinds according to the typ... View full abstract»

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  • A Summary and Update of Social Networks and Granular Computing

    Publication Year: 2010, Page(s):30 - 31
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (126 KB) | HTML iconHTML

    In summary, analyzing a social position from a GrC perspective has two major advantages. On the one hand, it extends the application scope of the GrC methodology. On the other hand, we can logically transform relational granulation of social networks into functional granulation; as a result, the well-developed techniques of data table analysis can be applied to social position analysis. Thus, this... View full abstract»

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  • Update and Illustration on Granular Computing: Practices, Theory and Future Directions

    Publication Year: 2010, Page(s):32 - 33
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (125 KB) | HTML iconHTML

    This is an update and summary of an article in the Encyclopedia of Complexity and Systems Science. View full abstract»

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  • Interval Type-2 Fuzzy Logic System versus Perceptual Computer: Similarities and Differences

    Publication Year: 2010, Page(s): 34
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (123 KB) | HTML iconHTML

    Summary form only given. Interval type-2 fuzzy logic systems (IT2 FLSs) fall into the category of "function approximation," and are widely used in fuzzy logic control, rule-based classification, signal processing, etc. On the other hand, a Perceptual Computer (Per-C) is an implementation of Zadeh's Computing With Words paradigm, and is used to aid people in making subjective judgments. This talk w... View full abstract»

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  • Granulation of Knowledge by Tools of Rough Mereology

    Publication Year: 2010, Page(s):35 - 36
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (224 KB) | HTML iconHTML

    This short note is an excerpt from the article `Granulation of Knowledge: Similarity Based Approach in Information and Decision Systems' in Springer Encyclopedia of Complexity and System Sciences by this author. We enclose here the very rudiments of rough mereological approach to knowledge granulation. View full abstract»

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  • Descriptive Granularity

    Publication Year: 2010, Page(s):37 - 38
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (124 KB) | HTML iconHTML

    We present here a formal syntax and semantics for a notion of a descriptive granularity. We do so in terms of three abstract models: Descriptive, Semantic, and Granular. The descriptive model formalizes the syntactical concepts and properties of the data mining, or learning process. Semantic model formalizes its semantical properties. The Granular model establishes a relationship between the Descr... View full abstract»

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  • A Summary of Granular Computing System Vulnerabilities: Exploring the Dark Side of Social Networking Communities

    Publication Year: 2010, Page(s):39 - 40
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (135 KB) | HTML iconHTML

    Online social networking communities are connecting hundreds of millions of individuals across the globe and facilitating new modes of interaction. Due to their immense popularity, an important question is whether these communities are safe for their users. In our encyclopedia article, we address this safety question and show that social networking communities are susceptible to numerous attacks. ... View full abstract»

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