IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Granular Computing and Human-Centricity in Computational Intelligence

Granular Computing and Human-Centricity in Computational Intelligence
View Sample PDF
Author(s): Witold Pedrycz (University of Alberta, Canada and Polish Academy of Sciences, Poland)
Copyright: 2012
Pages: 15
Source title: Machine Learning: Concepts, Methodologies, Tools and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-60960-818-7.ch702

Purchase

View Granular Computing and Human-Centricity in Computational Intelligence on the publisher's website for pricing and purchasing information.

Abstract

Information granules and ensuing Granular Computing offer interesting opportunities to endow processing with an important facet of human-centricity. This facet implies that the underlying processing supports non-numeric data inherently associated with the variable perception of humans. Systems that commonly become distributed and hierarchical, managing granular information in hierarchical and distributed architectures, is of growing interest, especially when invoking mechanisms of knowledge generation and knowledge sharing. The outstanding feature of human centricity of Granular Computing along with essential fuzzy set-based constructs constitutes the crux of this study. The author elaborates on some new directions of knowledge elicitation and quantification realized in the setting of fuzzy sets. With this regard, the paper concentrates on knowledge-based clustering. It is also emphasized that collaboration and reconciliation of locally available knowledge give rise to the concept of higher type information granules. Other interesting directions enhancing human centricity of computing with fuzzy sets deals with non-numeric semi-qualitative characterization of information granules, as well as inherent evolving capabilities of associated human-centric systems. The author discusses a suite of algorithms facilitating a qualitative assessment of fuzzy sets, formulates a series of associated optimization tasks guided by well-formulated performance indexes, and discusses the underlying essence of resulting solutions.

Related Content

Muhammad Naeem, Salman Memon, Anita Larik, Syed Rizwan Mehdi, Hasan Ahmed Faridi, Khalida Khan, Sana Zafar, Manoj Kumar. © 2026. 20 pages.
Imdad Ali Shah, N. Z. Jhanjhi. © 2026. 12 pages.
Hafsa Muzammal, Muhammad Zaman, Muhammad Safdar, Muhammad Adnan Shahid, Zuhaib Nishtar, Muhammad Bilal, Muntaha Munir, Mehar Muhammad Haseeb, Aamir Raza, Syed Intsar Hussain Shah, Usman Zafar, Nalain E. Muhammad, Hafiz Muhammad Bilawal Akram. © 2026. 30 pages.
Luminita Diaconu, Yassine Mouniane. © 2026. 32 pages.
Kumar J. Parmar, Tejas Chandulal Chauhan, T. Premavathi. © 2026. 32 pages.
Mahmoud Oudghiri, Mohamed El Bakkali, Yassine Mouniane, Nagla Abid, Samah Bouhassoun, Fatima-ezzahra Jaayefar, Fath Alah Elwahab, Issam El-Khadir, Ahmed Chriqui, Mohammed Ibriz. © 2026. 26 pages.
Issam El-Khadir, Yassine Mouniane, Ahmed Chriqui, Mohamed El Bakkali, Driss Hmouni. © 2026. 34 pages.
Body Bottom