Pre-shaped fuzzy c-means algorithm (PFCM) for transparent membership function generation

The fuzzy c-means algorithm (FCM) is widely used in the generation of membership functions from historical data. However, most of FCM-based membership function generation algorithms consider little on the transparency or the understandability of the resulting membership functions. In other words, th...

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Bibliographic Details
Main Authors: Long Chen, Chen, C.L.P.
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
Decision trees
Fuzzy sets
Fuzzy systems
Humans
Implants
Multiplexing
Neurons
Partitioning algorithms
Shape
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Description
Summary:The fuzzy c-means algorithm (FCM) is widely used in the generation of membership functions from historical data. However, most of FCM-based membership function generation algorithms consider little on the transparency or the understandability of the resulting membership functions. In other words, there is inconsistency in generating membership functions using traditional FCM algorithm. This paper proposes a pre-shaped fuzzy c-means algorithm (PFCM) to generate more transparent membership functions. PFCM will preserve the predefined transparent shapes of membership functions during the process of the optimization of the clustering algorithm. Numeric experiments based on data collected in a real project demonstrate the feasibility and superiority of the proposed new algorithm.
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2007.4413722