Human-Centric Information Processing Through Granular ModellingAndrzej Bargiela, Witold Pedrycz Information granules and their processing permeate a way in which we perceive the world, carryout processing at the conceptual (abstract) level, and communicate our findings to the surrounding environment. The importance of information granulation becomes even more apparent when we are faced with a rapidly growing flood of data, become challenged to make decisions in complex data settings and are required to appreciate the context from which the data is derived. Human centricity of systems that claim to be “intelligent” and the granular computing come hand in hand. It is not surprising at all to witness that the paradigm of Granular Computing has started to gain visibility and continues along this path by gathering interest from the circles of academics and practitioners. It is quite remarkable that the spectrum of application and research areas that have adopted information granulation as a successful strategy for dealing with information complexity covers such diverse fields as bioinformatics, image understanding, environmental monitoring, urban sustainability, to mention few most visible in the literature. Undoubtedly, there are two important aspects of Granular Computing that are worth stressing. First, there are several formalisms in which information granules are articulated so be intervals (sets), fuzzy sets, rough sets, soft sets, approximate sets, near sets and alike. They are complementary and each of them offers some interesting views at the complexity of the world and cyberspace. |
Contents
1 | |
Integrative Levels of Granularity | 31 |
Foundations and Perspectives | 49 |
Concept Granular Computing Based on Lattice Theoretic Setting | 67 |
Interpretability of Fuzzy Information Granules | 95 |
Semantic Driven Fuzzy Clustering for HumanCentric Information Processing Applications | 119 |
ManyValued Logic Tools for Granular Modeling | 152 |
Type2 Fuzzy Logic and the Modelling of Uncertainty in Applications | 185 |
Autonomous Composition of Fuzzy Granules in Ambient Intelligence Scenarios | 265 |
Information Processing in Biomedical Applications | 289 |
Gene Interactions Subnetworks and Soft Computing | 312 |
Uncertain Identification Problems in the Context of Granular Computing | 329 |
Visualizing Huge Image Databases by Formal Concept Analysis | 351 |
Multifaceted Perspectives | 374 |
397 | |
402 | |
Interval Type2 Fuzzy Logic Applications | 203 |
Processing of Information Microgranules within an Individuals Society | 232 |
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Human-Centric Information Processing Through Granular Modelling Andrzej Bargiela,Witold Pedrycz No preview available - 2010 |
Common terms and phrases
abstraction algebraic antigen applications approach approximation space Apri(X artificial neural networks attributes Bargiela biclustering BL-algebras cell classification complex concept granular computing concept lattice constraints data set defined denote element environment evolutionary evolutionary algorithm example feature formal concept analysis formal context fuzzy clustering fuzzy control fuzzy information granules fuzzy relation Fuzzy Systems granular computing granular computing system Heidelberg hierarchical human Human-Centric Information Processing IEEE Trans inference information granules input integration Intelligence interaction interpretability knowledge levels linguistic mathematical membership functions method neural networks neuron objects operator optimization outliers output paradigm parameters partition patterns Pedrycz perceptual system problem Proc RDQL relation equations representation represented robot rough set rules samples semantic signal Skowron Springer stochastic strategy structure t-norm Table techniques Theorem threshold tion truth degree type-2 fuzzy logic type-2 fuzzy sets users values variable Zadeh