Granular computing: Models and applications

Han, Jiawei; Lin, Tsau Young

doi:10.1002/int.20390

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…In the past few years, there are a renewed and fast growing interest in GrC. Granular computing has begun to play important roles in bioinformatics, e-Business, security, machine learning, data mining, high-performance computing and wireless mobile computing in terms of efficiency, effectiveness, robustness and uncertainty [3,4,5].…”

Section: Related Workmentioning

confidence: 99%

Granular Computing Model Based on Quantum Computing Theory

Guan

2014

2014 Tenth International Conference on Computational Intelligence and Security

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This paper firstly introduces quantum computing theory into the study of granular computing, then puts forward a new granular computing model based on quantum computing theory, which fully makes full use of the parallelism of quantum computing and layered characteristics of the granular computing. As Grover quantum algorithm has some shortcomings in the classical target number of solutions to solve more problems in the search space, this paper further puts forward an improved Grover quantum search algorithm, and experiment results show it is effective.

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Section: Related Workmentioning

confidence: 99%

Granular Computing Model Based on Quantum Computing Theory

Guan

2014

2014 Tenth International Conference on Computational Intelligence and Security

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“…Granular computing deals with the processing of complex problems from two different aspects [21]. The first aspect is a problem granulation that granulates a complex problem into granules, including the construction, representation, and interpretation of granules, while the second aspect is to compute and reason these granular entities in terms of intrarelationships within these entities and interrelationships or interactions between them.…”

Section: Graunlar Computingmentioning

confidence: 99%

Granularity-Based User-Centric Multi-Strategies and Application in Knowledge Retrieval

Sun¹,

Zhong²,

Xu³

2013

JCP

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Granular computing is a general computing paradigm of problem solving for effectively using granules in problem solving. From the viewpoint of granularity, this paper presents a new granular computing data cycle model in which information granule construction, granular space, information granule operations, and knowledge acquisition approach are proposed respectively. To do a more user oriented way, a multi-strategies model, namely, Base-Level learning strategy, user interest retention strategy, and starting point strategy, introduces to unify search and reasoning for effective problem solving. Then, we discuss granularity-based unification using three strategies mentioned above and show three-levels of granularity from human problem solving to a wide variety of user needs satisfied. Furthermore, on the basis of user-centric multistrategies and granular information processing, we develop a conceptual framework of granularity-based knowledge retrieval model, which enlarges the application areas of granular computing.

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“…Granular computing (Pedrycz and Chen 2014;Pedrycz 2013;Han and Lin 2010;Bargiela and Pedrycz 2008) is typically portrayed as a research context intended as a convergence of various modeling and computational approaches for dealing with uncertainty. The modeling side is essentially rooted on formal constructs called information granules (IG) .…”

Section: Introductionmentioning

confidence: 99%

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

Livi

Sadeghian

2015

Granul. Comput.

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Data granulation emerged as an important paradigm in modeling and computing with uncertainty, exploiting information granules as the main mathematical constructs involved in the context of granular computing. In this paper, we comment on the importance of data granulation in computational intelligence methods. Toward this aim, we discuss also the peculiar aspects related to the analysis of non-geometric patterns, which have recently attracted considerable attention by researchers. As a conclusion, we elaborate over the fundamental, conceptual problems underlying the process of data granulation, which drive the quest for a sound theory of granular computing.

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Granular computing: Models and applications

Cited by 8 publications

References 20 publications

Granular Computing Model Based on Quantum Computing Theory

Granular Computing Model Based on Quantum Computing Theory

Granularity-Based User-Centric Multi-Strategies and Application in Knowledge Retrieval

Granular computing, computational intelligence, and the analysis of non-geometric input spaces

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