Granular Computing | Part 1
A New Problem-Solving Paradigm
The concept of Granular Computing (GrC) was initially called information granularity or information granulation and was introduced by Zadeh [1] in 1979. The term granular computing (GrC) first appeared in 1997 by Zadeh [2].
It is generally accepted that 1997 is considered as the birth of granular computing. Although the term “Granular Computing” is new, the basic notions and principles of granular computing occurred under various forms in many disciplines and fields.
Since then, it has drawn the interest and attention of many researchers and witnessed rapid development and research growth.
Definition 1: Granular computing can be defined as an umbrella term to cover any theories, methodologies, techniques, and tools that make use of granules in complex problem-solving. The process of performing computation and operations on granules
Definition 2: GrC is a general computation theory that imitates human thinking and reasoning by dealing with information as a form of aggregates called information granules
Prof. Y. Y. Yao views granular computing as a complementary and dependent triangle: structured thinking from a philosophical perspective, structured problem-solving from a methodological perspective, and structured information processing from a computational perspective.
Definition 3: GrC is a general computation theory for effectively using granules such as classes, clusters, subsets, groups, and intervals to build an efficient computational model for complex applications with huge amounts of data, information, and knowledge.
Example: Imagine you are reading a scientific paper, you can easily observe multiple levels of processing information, as illustrated in the figure below:
Terminology Alert!
Granule: It refers to a coherent and meaningful unit that encapsulates information. It represents a chunk or cluster of data that exhibits internal homogeneity and external heterogeneity. Granules can be thought of as building blocks that help organize and structure complex information. (Need for information abstraction)
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Granularity: It refers to the level or scale at which information is organized, represented, or processed. It pertains to the degree of detail or coarseness in the division of data into granules. Granularity determines the size or extent of the individual granules and how they relate to each other in terms of their hierarchical or overlapping nature. (Knowledge of abstraction level)
That’s all for now, granular computing (GrC) is a broad topic that can not be covered in one blog, which is why I will be considering writing multiple articles in the future, trying to cover the most important aspects of GrC.
References:
[1] Zadeh, Lotfi A. “Fuzzy sets and information granularity.” Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers (1979): 433–448.
[2] Zadeh, Lotfi A. “Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic.” Fuzzy sets and systems 90.2 (1997): 111–127.
