Results 261 to 270 of about 67,600 (304)

Clustering of Information Granules in Hotspot Identification

2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019
Conceptually and algorithmically, hotspots could be regarded as information granules. In this study, we propose an aggregation of Fuzzy C-Means (FCM) algorithm and the principle of justifiable granularity (PJG) as a new approach to forming hotspots. With the proposed method, the quality of the hotspots formed in this manner could also be provided as an
Yinghua Shen, Witold Pedrycz, Ronei Marcos de Moraes, Xingchen Hu, Xianmin Wang, Adam Gacek   +5 more
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Collaborative Wireless Information Granulation Architecture

2008 Second Asia International Conference on Modelling & Simulation (AMS), 2008
As we move further into the age of machine intelligence and automated reasoning, one daunting problem becomes harder and harder to master. The problem is - how can we cope with the explosive growth in data, information, and knowledge? How can we locate and infer from decision- relevant information that is embedded in a large database that is ...
Evtim Peytchev, Ismail Kucukdurgut
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Information granules in general and complete coverings

2005 IEEE International Conference on Granular Computing, 2005
Two different operations of information granules are defined in the general coverings on a universe in this paper and different levels of granularity of coverings are studied based on the operations. Besides, general covering and complete covering according to a compatible relation are established in rough set theory in incomplete information systems ...
Chen Wu, Xibei Yang
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Generalizations of Information Granules

2003
In this chapter, we discuss various extensions of the fundamental formal environments of granular computing and elaborate on a series of synergistic interactions arising between them. The first trend is motivated by the complexity and diversity of the granular aspect of information.
Andrzej Bargiela, Witold Pedrycz
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Information Granules for Intelligent Knowledge Structures

2007
The premise of this paper is that the acquisition, aggregation, merging and use of information requires some new ideas, tools and techniques which can simplify the construction, analysis and use of what we call ephemeral knowledge structures. Ephemeral knowledge structures are used and constructed by granular agents.
Patrick Doherty 0001, Witold Lukaszewicz, Andrzej Szalas   +2 more
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Interpretability constraints for fuzzy information granulation

Information Sciences, 2008
Information granules are complex entities that arise in the process of abstraction of data and derivation of knowledge. The automatic generation of information granules from data is an important task, since it gives to machines the ability of acquiring knowledge that can be communicated to users.
Corrado Mencar, Anna Maria Fanelli
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Information Granules in Application to Image Recognition

2015
The paper concerns specific problems of color digital image recognition by use of the concept of fuzzy and rough granulation. This idea employs information granules that contain pieces of knowledge about digital pictures such as color, location, size, and shape of an object to be recognized.
Krzysztof Wiaderek, Danuta Rutkowska, Elisabeth Rakus-Andersson   +2 more
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Theory and Practice on Information Granule Matrix

2006
In this paper, a new framework called information granule matrix is suggested to illustrate a given granule sample for showing its information structure. The new framework does not any extra condition but the observations. An information granule matrix can be turned into a fuzzy relation matrix for fuzzy inference.
Ye Xue, Chongfu Huang
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Information granules in image histogram analysis

Computerized Medical Imaging and Graphics, 2018
A concept of granular computing employed in intensity-based image enhancement is discussed. First, a weighted granular computing idea is introduced. Then, the implementation of this term in the image processing area is presented. Finally, multidimensional granular histogram analysis is introduced.
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An Improved Clustering Algorithm for Information Granulation

2005
C-means clustering is a popular technique to classify unlabeled data into dif-ferent categories. Hard c-means (HCM), fuzzy c-means (FCM) and rough c-means (RCM) were proposed for various applications. In this paper a fuzzy rough c-means algorithm (FRCM) is present, which integrates the advantage of fuzzy set theory and rough set theory. Each cluster is
Qinghua Hu, Daren Yu
openaire   +1 more source