The Principle of Justifiable Granularity and an Optimization of Information Granularity Allocation as Fundamentals of Granular Computing

Witold Pedrycz
Volume: 7, No: 3, Page: 397 ~ 412, Year: 2011
10.3745/JIPS.2011.7.3.397
Keywords: Information Granularity, Principle of Justifiable Granularity, Knowledge Management, Optimal Granularity Allocation
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Abstract
Granular Computing has emerged as a unified and coherent framework of designing, processing, and interpretation of information granules. Information granules are formalized within various frameworks such as sets (interval mathematics), fuzzy sets, rough sets, shadowed sets, probabilities (probability density functions), to name several the most visible approaches. In spite of the apparent diversity of the existing formalisms, there are some underlying commonalities articulated in terms of the fundamentals, algorithmic developments and ensuing application domains. In this study, we introduce two pivotal concepts: a principle of justifiable granularity and a method of an optimal information allocation where information granularity is regarded as an important design asset. We show that these two concepts are relevant to various formal setups of information granularity and offer constructs supporting the design of information granules and their processing. A suite of applied studies is focused on knowledge management in which case we identify several key categories of schemes present there.

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Cite this article
IEEE Style
Witold Pedrycz, "The Principle of Justifiable Granularity and an Optimization of Information Granularity Allocation as Fundamentals of Granular Computing," Journal of Information Processing Systems, vol. 7, no. 3, pp. 397~412, 2011. DOI: 10.3745/JIPS.2011.7.3.397.

ACM Style
Witold Pedrycz, "The Principle of Justifiable Granularity and an Optimization of Information Granularity Allocation as Fundamentals of Granular Computing," Journal of Information Processing Systems, 7, 3, (2011), 397~412. DOI: 10.3745/JIPS.2011.7.3.397.