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The art of granular computing:
 Proceeding of the International Conference on Rough Sets and Emerging Intelligent Systems Paradigms,
, 2007
"... Abstract: This paper has two purposes. One is to present a critical examination of the rise of granular computing and the other is to suggest a triarchic theory of granular computing. By examining the reasons, justifications, and motivations for the rise of granular computing, we may be able to ful ..."
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Cited by 74 (20 self)
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Abstract: This paper has two purposes. One is to present a critical examination of the rise of granular computing and the other is to suggest a triarchic theory of granular computing. By examining the reasons, justifications, and motivations for the rise of granular computing, we may be able to fully appreciate its scope, goal and potential values. The results enable us to formulate a triarchic theory in the light of research results from many disciplines. The three components of the theory are labeled as the philosophy, the methodology, and the computation. The integration of the three offers a unified view of granular computing as a way of structured thinking, a method of structured problem solving, and a paradigm of structured information processing, focusing on hierarchical granular structures. The triarchic theory is an important effort in synthesizing the various theories and models of granular computing. Key words: Triarchic theory of granular computing; systems theory; structured thinking, problem solving and information processing. CLC number: Document code: A Introduction Although granular computing, as a separate field of study, started a decade ago [1], its basic philosophy, ideas, principles, methodologies, theories and tools has, in fact, long been used either explicitly or implicitly across many branches of natural and social sciences The answers, at least partial answers, to these questions may be obtained by drawing and synthesizing results from wellestablished disciplines, including philosophy, psychology, neuroscience, cognitive science, education, artificial intelligence, computer programming, and many more. Previously, I argued that granular computing represents an idea converged from many branches of natural and social sciences HumanInspired Computing Research on understanding the human brain and natural intelligence is closely related to the field of artificial intelligence (AI) and information technology (IT). The results have led to a computational view for explaining how the mind works
On Modeling Data Mining with Granular Computing
 Proceedings of COMPSAC 2001
, 2001
"... The main objective of this paper is to advocate for formal and mathematical modeling of data mining, which unfortunately has not received much attention. A framework is proposed for rule mining based on granular computing. It is developed in the Tarski's style through the notions of a model and ..."
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Cited by 42 (19 self)
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The main objective of this paper is to advocate for formal and mathematical modeling of data mining, which unfortunately has not received much attention. A framework is proposed for rule mining based on granular computing. It is developed in the Tarski's style through the notions of a model and satisfiability. The model is a database consisting of a finite set of objects described by a finite set of attributes. Within this framework, a concept is defined as a pair consisting of the intension, an expression in a certain language over the set of attributes, and the extension, a subset of the universe, of the concept. An object satisfies the expression of a concept if the object has the properties as specified by the expression, and the object belongs to the extension of the concepts. Rules are used to describe relationships between concepts. A rule is expressed in terms of the intensions of the two concepts and is interpreted in terms of the extensions of the concepts. Two interpretations of rules are examined in detail, one is based on logical implication and the other on conditional probability.
A generalized decision logic language for granular computing
 In Proceedings of the 11th IEEE International Conference on Fuzzy Systems
, 2002
"... Abstract A generalized decision logic language GDL is proposed for granular computing (GrC) in the Tarski’s style through the notions of a model and satisfiability. The model is an information table consisting of a finite set of objects described by a finite set of attributes. A concept or a granul ..."
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Cited by 19 (12 self)
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Abstract A generalized decision logic language GDL is proposed for granular computing (GrC) in the Tarski’s style through the notions of a model and satisfiability. The model is an information table consisting of a finite set of objects described by a finite set of attributes. A concept or a granule is characterized by a pair consisting of the intension of the concept, a formula of the language GDL, and the extension of the concept, a subset of the universe. We discuss the application of GDL in formal concepts and decision rules. The former deals with description and interpretation of granules, and the latter deals with the relationships between granules. I.
Granular Computing Using Information Tables
 In: Data Mining, Rough Sets and Granular Computing
, 2002
"... Abstract. A simple and more concrete granular computing model may be developed using the notion of information tables. In this framework, each object in a finite nonempty universe is described by a finite set of attributes. Based on attribute values of objects, one may decompose the universe into pa ..."
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Cited by 19 (10 self)
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Abstract. A simple and more concrete granular computing model may be developed using the notion of information tables. In this framework, each object in a finite nonempty universe is described by a finite set of attributes. Based on attribute values of objects, one may decompose the universe into parts called granules. Objects in each granule share the same or similar description in terms of their attribute values. Studies along this line have been carried out in the theories of rough sets and databases. Within the proposed model, this paper reviews the pertinent existing results and presents their generalizations and applications. 1
Granular Computing as a Basis for Consistent Classification Problems
 Proceedings PAKDD’02 Workshop on Foundations of Data Mining
, 2002
"... Within a granular computing model of data mining, we reformulate the consistent classification problems. The granulation structures are partitions of a universe. A solution to a consistent classification problem is a definable partition. Such a solution can be obtained by searching a particular part ..."
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Cited by 17 (4 self)
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Within a granular computing model of data mining, we reformulate the consistent classification problems. The granulation structures are partitions of a universe. A solution to a consistent classification problem is a definable partition. Such a solution can be obtained by searching a particular partition lattice. The new formulation enables us to precisely and concisely define many notions, and to present a more general framework for classification.
Information granulation and approximation in a decisiontheoretical model of rough sets
, 2003
"... Summary. Granulation of the universe and approximation of concepts in the granulated universe are two related fundamental issues in the theory of rough sets. Many proposals dealing with the two issues have been made and studied extensively. We present a critical review of results from existing studi ..."
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Cited by 13 (8 self)
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Summary. Granulation of the universe and approximation of concepts in the granulated universe are two related fundamental issues in the theory of rough sets. Many proposals dealing with the two issues have been made and studied extensively. We present a critical review of results from existing studies that are relevant to a decisiontheoretic modeling of rough sets. Two granulation structures are studied, one is a partition induced by an equivalence relation and the other is a covering induced by a reflexive relation. With respect to the two granulated views of the universe, element oriented and granule oriented definitions and interpretations of lower and upper approximation operators are examined. The structures of the families of fixed points of approximation operators are investigated. We start with the notions of rough membership functions and graded set inclusion defined by conditional probability. This enables us to examine different granulation structures and the induced approximations in a decisiontheoretic setting. By reviewing and combining results from existing studies, we attempt to establish a solid foundation for rough sets and to provide a systematic way for determining the required parameters in defining approximation operators. 1
A Measurementtheoretic foundation for rule interestingness evaluation
 Proceedings of Workshop on Foundations and New Directions in Data Mining in the Third IEEE International Conference on Data Mining (ICDM 2003
, 2003
"... Summary. Many measures have been proposed and studied extensively in data mining for evaluating the interestingness (or usefulness) of discovered rules. They are usually defined based on structural characteristics or statistical information about the rules. The meaningfulness of each measure was int ..."
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Cited by 10 (7 self)
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Summary. Many measures have been proposed and studied extensively in data mining for evaluating the interestingness (or usefulness) of discovered rules. They are usually defined based on structural characteristics or statistical information about the rules. The meaningfulness of each measure was interpreted based either on intuitive arguments or mathematical properties. There does not exist a framework in which one is able to represent the user judgment explicitly, precisely, and formally. Since the usefulness of discovered rules must be eventually judged by users, a framework that takes user preference or judgement into consideration will be very valuable. The objective of this paper is to propose such a framework based on the notion of user preference. The results are useful in establishing a measurementtheoretic foundation of rule interestingness evaluation.
Granular computing for data mining
 Proceedings of SPIE Conference on Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security
, 2006
"... Granular computing, as an emerging research field, provides a conceptual framework for studying many issues in data mining. This paper examines some of those issues, including data and knowledge representation and processing. It is demonstrated that one of the fundamental tasks of data mining is sea ..."
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Cited by 7 (2 self)
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Granular computing, as an emerging research field, provides a conceptual framework for studying many issues in data mining. This paper examines some of those issues, including data and knowledge representation and processing. It is demonstrated that one of the fundamental tasks of data mining is searching for the right level of granularity in data and knowledge representation. 1.
Computational Web Intelligence (CWI): Synergy of Computational Intelligence and Web Technology
 Proc. of FUZZIEEE2002 of World Congress of Computational Intelligence 2002: Special Session on Computational Web Intelligence
, 2002
"... With explosive growth of eBusiness on the Internet, and wireless networks, users face more and more challenging networksbased application problems in terms of intelligent eApplications. To increase the QoI (Quality of Intelligence) of eBusiness, we propose a new research area called Computational ..."
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Cited by 4 (3 self)
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With explosive growth of eBusiness on the Internet, and wireless networks, users face more and more challenging networksbased application problems in terms of intelligent eApplications. To increase the QoI (Quality of Intelligence) of eBusiness, we propose a new research area called Computational Web Intelligence (CWI) based on both Computational Intelligence (CI) and Web Technology (WT). Generally, the intelligent ebrainware using CWI techniques plays an important role in smart eBusiness. In this paper, fundamental concepts, basic methods, major applications and future trends of CWI are described to briefly show a general framework of CWI from different aspects.