Information Granularity, Information Granules, and Granular Computing

2018 ◽  
pp. 1-18
Author(s):  
Witold Pedrycz
Keyword(s):  
Granular Computing ◽  
Information Granularity ◽  
Information Granules

Human Centricity and Perception-Based Perspective and Their Centrality to the Agenda of Granular Computing

2011 ◽  
Vol 5 (4) ◽  
pp. 44-60 ◽  
Author(s):  
Witold Pedrycz
Keyword(s):  
Decision Making ◽  
Intelligent Systems ◽  
Granular Computing ◽  
Optimal Allocation ◽  
Information Granularity ◽  
Analytic Hierarchy ◽  
Information Granules ◽  
Decision Making Processes ◽  
Collaborative Activities ◽  
Hierarchy Process

In spite of their striking diversity, numerous tasks and architectures of intelligent systems such as those permeating multivariable data analysis, decision-making processes along with their underlying models, recommender systems and others exhibit two evident commonalities. They promote (a) human centricity and (b) vigorously engage perceptions (rather than plain numeric entities) in the realization of the systems and their further usage. Information granules play a pivotal role in such settings. Granular Computing delivers a cohesive framework supporting a formation of information granules and facilitating their processing. The author exploits two essential concepts of Granular Computing. The first one deals with the construction of information granules. The second one helps endow constructs of intelligent systems with a much needed conceptual and modeling flexibility. The study elaborates in detail on the three representative studies. In the first study being focused on the Analytic Hierarchy Process (AHP) used in decision-making, the author shows how an optimal allocation of granularity helps improve the quality of the solution and facilitate collaborative activities in models of group decision-making. The second study is concerned with a granular interpretation of temporal data where the role of information granularity is profoundly visible when effectively supporting human centric description of relationships existing in data. The third study concerns a formation of granular logic descriptors on a basis of a family of logic descriptors.

KNOWLEDGE MANAGEMENT AND SEMANTIC MODELING: A ROLE OF INFORMATION GRANULARITY

2013 ◽  
Vol 23 (01) ◽  
pp. 5-11 ◽  
Author(s):  
WITOLD PEDRYCZ
Keyword(s):  
Knowledge Management ◽  
Knowledge Representation ◽  
Granular Computing ◽  
Semantic Modeling ◽  
Information Granularity ◽  
Information Granules ◽  
Level Of Abstraction ◽  
Role Of Information

In this study, we highlight some fundamental issues of knowledge management and cast them in the setting of Granular Computing (GrC). We show how its formal constructs — information granules are instrumental in knowledge representation and specification of its level of abstraction.

Human Centricity and Perception-Based Perspective and Their Centrality to the Agenda of Granular Computing

2012 ◽  
pp. 178-193 ◽  
Author(s):  
Witold Pedrycz
Keyword(s):  
Decision Making ◽  
Intelligent Systems ◽  
Granular Computing ◽  
Optimal Allocation ◽  
Information Granularity ◽  
Analytic Hierarchy ◽  
Information Granules ◽  
Decision Making Processes ◽  
Collaborative Activities ◽  
Hierarchy Process

In spite of their striking diversity, numerous tasks and architectures of intelligent systems such as those permeating multivariable data analysis, decision-making processes along with their underlying models, recommender systems and others exhibit two evident commonalities. They promote (a) human centricity and (b) vigorously engage perceptions (rather than plain numeric entities) in the realization of the systems and their further usage. Information granules play a pivotal role in such settings. Granular Computing delivers a cohesive framework supporting a formation of information granules and facilitating their processing. The author exploits two essential concepts of Granular Computing. The first one deals with the construction of information granules. The second one helps endow constructs of intelligent systems with a much needed conceptual and modeling flexibility. The study elaborates in detail on the three representative studies. In the first study being focused on the Analytic Hierarchy Process (AHP) used in decision-making, the author shows how an optimal allocation of granularity helps improve the quality of the solution and facilitate collaborative activities in models of group decision-making. The second study is concerned with a granular interpretation of temporal data where the role of information granularity is profoundly visible when effectively supporting human centric description of relationships existing in data. The third study concerns a formation of granular logic descriptors on a basis of a family of logic descriptors.

Multi-Granular Computing through Rough Sets

2014 ◽  
pp. 1-34 ◽  
Author(s):  
B. K. Tripathy
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Equivalence Relation ◽  
Rough Sets ◽  
Intelligent Systems ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Point Of View ◽  
Information Granules ◽  
Computing Platform

Granular Computing has emerged as a framework in which information granules are represented and manipulated by intelligent systems. Granular Computing forms a unified conceptual and computing platform. Rough set theory put forth by Pawlak is based upon single equivalence relation taken at a time. Therefore, from a granular computing point of view, it is single granular computing. In 2006, Qiang et al. introduced a multi-granular computing using rough set, which was called optimistic multigranular rough sets after the introduction of another type of multigranular computing using rough sets called pessimistic multigranular rough sets being introduced by them in 2010. Since then, several properties of multigranulations have been studied. In addition, these basic notions on multigranular rough sets have been introduced. Some of these, called the Neighborhood-Based Multigranular Rough Sets (NMGRS) and the Covering-Based Multigranular Rough Sets (CBMGRS), have been added recently. In this chapter, the authors discuss all these topics on multigranular computing and suggest some problems for further study.

Granular Computing and Human-Centricity in Computational Intelligence

2012 ◽  
pp. 13-27
Author(s):  
Witold Pedrycz
Keyword(s):  
Fuzzy Sets ◽  
Granular Computing ◽  
Knowledge Generation ◽  
Qualitative Assessment ◽  
Information Granules ◽  
Knowledge Based ◽  
Performance Indexes ◽  
Numeric Data ◽  
Type Information

Information granules and ensuing Granular Computing offer interesting opportunities to endow processing with an important facet of human-centricity. This facet implies that the underlying processing supports non-numeric data inherently associated with the variable perception of humans. Systems that commonly become distributed and hierarchical, managing granular information in hierarchical and distributed architectures, is of growing interest, especially when invoking mechanisms of knowledge generation and knowledge sharing. The outstanding feature of human centricity of Granular Computing along with essential fuzzy set-based constructs constitutes the crux of this study. The author elaborates on some new directions of knowledge elicitation and quantification realized in the setting of fuzzy sets. With this regard, the paper concentrates on knowledge-based clustering. It is also emphasized that collaboration and reconciliation of locally available knowledge give rise to the concept of higher type information granules. Other interesting directions enhancing human centricity of computing with fuzzy sets deals with non-numeric semi-qualitative characterization of information granules, as well as inherent evolving capabilities of associated human-centric systems. The author discusses a suite of algorithms facilitating a qualitative assessment of fuzzy sets, formulates a series of associated optimization tasks guided by well-formulated performance indexes, and discusses the underlying essence of resulting solutions.

scholarly journals Practical Employment of Granular Computing to Complex Application Layer Cyberattack Detection

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Rafał Kozik ◽  
Marek Pawlicki ◽  
Michał Choraś ◽  
Witold Pedrycz
Keyword(s):  
Machine Learning ◽  
Homeland Security ◽  
Scientific Community ◽  
Granular Computing ◽  
Network Data ◽  
Application Layer ◽  
Practical Application ◽  
Information Granules ◽  
Machine Learning Methods ◽  
Novel Approach

Network and information security are regarded as some of the most pressing problems of contemporary economy, affecting both individual citizens and entire societies, making them a highlight for homeland security. Innovative approaches to handle this challenge are undertaken by the scientific community, proposing the utilization of the emerging, advanced machine learning methods. This very paper puts forward a novel approach to the detection of cyberattacks taking inventory of the practical application of information granules. The feasibility of utilizing Granular Computing (GC) as a solution to the most current challenges in cybersecurity is researched. To the best of our knowledge, granular computing has not yet been widely examined or used for cybersecurity application purposes. The major contribution of this work is a method for constructing information granules from network data. We then report promising results on a benchmark dataset.

Granular Computing and Human-Centricity in Computational Intelligence

2012 ◽  
pp. 1721-1735
Author(s):  
Witold Pedrycz
Keyword(s):  
Fuzzy Sets ◽  
Granular Computing ◽  
Knowledge Generation ◽  
Qualitative Assessment ◽  
Information Granules ◽  
Knowledge Based ◽  
Performance Indexes ◽  
Numeric Data ◽  
Type Information

Information granules and ensuing Granular Computing offer interesting opportunities to endow processing with an important facet of human-centricity. This facet implies that the underlying processing supports non-numeric data inherently associated with the variable perception of humans. Systems that commonly become distributed and hierarchical, managing granular information in hierarchical and distributed architectures, is of growing interest, especially when invoking mechanisms of knowledge generation and knowledge sharing. The outstanding feature of human centricity of Granular Computing along with essential fuzzy set-based constructs constitutes the crux of this study. The author elaborates on some new directions of knowledge elicitation and quantification realized in the setting of fuzzy sets. With this regard, the paper concentrates on knowledge-based clustering. It is also emphasized that collaboration and reconciliation of locally available knowledge give rise to the concept of higher type information granules. Other interesting directions enhancing human centricity of computing with fuzzy sets deals with non-numeric semi-qualitative characterization of information granules, as well as inherent evolving capabilities of associated human-centric systems. The author discusses a suite of algorithms facilitating a qualitative assessment of fuzzy sets, formulates a series of associated optimization tasks guided by well-formulated performance indexes, and discusses the underlying essence of resulting solutions.

scholarly journals Hierarchies and Uncertainty Measures on Pythagorean Fuzzy Approximation Spaces

2020 ◽  
Author(s):  
Xiaojing Luo ◽  
Jingjing Song ◽  
Huili Dou ◽  
Xibei Yang ◽  
Taihua Xu
Keyword(s):  
Lattice Structure ◽  
Order Relation ◽  
Fuzzy Information ◽  
Information Granularity ◽  
Approximation Spaces ◽  
Information Granules ◽  
First Order ◽  
Fuzzy Approximation ◽  
Uncertainty Measures ◽  
The Relationship

In Granular Computing, the hierarchies and uncertainty measures are two important concepts to investigate the granular structures and uncertainty of approximation spaces. In this paper, hierarchies and uncertainty measures on pythagorean fuzzy approximation spaces will be researched. Firstly, the introduction and operations of pythagorean fuzzy granular structures are given, and three hierarchies and a lattice structure on pythagorean fuzzy approximation spaces are examined. The hierarchies are characterized by three order relations, the first order relation is defined on the inclusion relation of pythagorean fuzzy information granules, the second one is defined on the cardinality of pythagorean fuzzy information granules, and the third one is defined on the sum of the cardinality of pythagorean fuzzy information granules. The lattice structure is constructed on the first order relation on pythagorean fuzzy approximation spaces. Fuzzy information granularity and fuzzy information entropy are extended to describe the uncertainty of pythagorean fuzzy granular structures, and the relationship between the uncertainty measures and hierarchies are discussed. The examples show that hierarchies are effective to analyze the relationships among all granular structures on pythagorean fuzzy approximation spaces.

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