Human-Centric Information Processing Through Granular Modelling

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Andrzej Bargiela, Witold Pedrycz
Springer, Dec 28, 2008 - Technology & Engineering - 404 pages
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

RoughGranular Computing in HumanCentric Information Processing
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
Index
397
Author Index
402

Interval Type2 Fuzzy Logic Applications
203
Processing of Information Microgranules within an Individuals Society
232

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