Classification and clustering of granular data

A. Bargiela; W. Pedrycz

doi:10.1109/NAFIPS.2001.943807

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Conference proceeding

Classification and clustering of granular data

A. Bargiela and W. Pedrycz

Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), Vol.3, pp.1696-1701 vol.3

2001

DOI: https://doi.org/10.1109/NAFIPS.2001.943807

Abstract

Clustering algorithms

Computerized monitoring

Cost function

Data structures

Data visualization

Information analysis

Robust control

Robustness

Shape measurement

Topology

Information granules are formed to reduce the complexity of the description of real-world systems. The improved generality of information granules is attained through sacrificing some of the numerical precision of point-data. In this study we consider a hyperbox-based clustering and classification of granular data, and discuss detailed criteria for the assessment of the quality of the combined classification and clustering. The robustness of the criteria is assessed on both synthetic data and real-life data from the domain of traffic control.

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Details

Title: Classification and clustering of granular data
Creators - without role: A. Bargiela - University of Nottingham
W. Pedrycz - University of Alberta
Publication Details: Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), Vol.3, pp.1696-1701 vol.3
Publisher: IEEE
Identifiers: 9935984408331
Academic Unit: King Abdulaziz University
Language: English
Resource Type: Conference proceeding