On the optimal choice of parameters in a fuzzy c-means algorithm

Choe, Hyeong Sik; Jordan, Jay B.

doi:10.1109/fuzzy.1992.258640

Cited by 47 publications

(21 citation statements)

References 6 publications

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“…Bzddekde [17] claimed that a better clustering will be achieved when m ranges from 1.5 to 3.0. Choe and Jordan [18] found that the algorithm is not susceptive to m when it ranges from 8 to 30. Cannon [19,20] …”

Section: The Degree Of Fuzziness Choicementioning

confidence: 99%

On the optimal choice of parameters in using fuzzy clustering algorithm for segmentation of plant disease leaf images

Yancheng

Mao

et al. 2006

SPIE Proceedings

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As an important classifier, fuzzy c-means clustering technique has been widely used in segmentation of image. It is an adaptive segmentation method for plant disease images. However, it has some uncertain factors, when it is used for specific segmentation problem, that are input parameters value. The input parameters include the feature of the date set, the optimal number of cluster, and the degree of fuzziness. These parameters affect the speed and precision of fuzzy clustering segmentation. In this paper, the optimal choice of parameters in a fuzzy c-means algorithm for segmentation of plant disease image was discussed and investigated. Using the pixels gray and means of neighborhood pixels as input feature data; an adapting the FCM algorithm parameters based on fuzzy partition entropy, fuzzy partition coefficient, and compactness measures was used to choose the optimal cluster number; and experiments was used for choosing the degree of fuzziness. The Results show that the optimal clustering number for disease leaf segmentation problem is 4 and the degree of fuzziness is 2.

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Section: The Degree Of Fuzziness Choicementioning

confidence: 99%

On the optimal choice of parameters in using fuzzy clustering algorithm for segmentation of plant disease leaf images

Yancheng

Mao

et al. 2006

SPIE Proceedings

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“…Lin und Lee (1996), Chen (1996). AnschlieBend werden zwei fuzzyfizierte Netze, Fuzzy-Assoziativspeicher und Fuzzy-Kohonen-Netze kurz beschrieben.…”

Section: Fuzzy-neuronale Netzeunclassified

Fuzzy-Bildverarbeitung

Tizhoosh¹

1998

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“…There are several methods of fuzzy clustering (Yuan et al, 1995;Kamei et al, 1992;Choe and Jordan, 1992;Cheng et al, 1995;Berks et al, 2000), out of which, FCM clustering is the most popular one, due to not only its flexibility and robustness (Bezdek, 1974) but also its ability to render reliable and steady clusters, that is desired in a data mining research (Murase et al, 2004). It is an iterative approach, where data points are members of the clusters (whose numbers are pre-defined) with some membership values (i.e., degrees of belongingness).…”

Section: Introductionmentioning

confidence: 99%

Some studies on fuzzy clustering of psychosis data

Chattopadhyay¹,

Pratihar

Sarkar

2007

IJBIDM

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Clustering is a well-known method of data mining, which aims at extracting useful information from a data set. Clusters could be either crisp (having well-defined boundaries) or fuzzy (with vague boundaries) in nature. The present paper deals with fuzzy clustering of psychosis data. A set of statistically generated psychosis data are clustered using Fuzzy C-Means (FCM) algorithm and entropy-based method and its proposed extensions. From the clusters, we finally decide on patient distributions response-wise. Comparisons are made of the above algorithms, in terms of quality of clusters made and their computational complexity. Finally, the multidimensional best set of clusters are mapped into 2-D for visualisation, using a Self-Organising Map (SOM).

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On the optimal choice of parameters in a fuzzy c-means algorithm

Cited by 47 publications

References 6 publications

On the optimal choice of parameters in using fuzzy clustering algorithm for segmentation of plant disease leaf images

On the optimal choice of parameters in using fuzzy clustering algorithm for segmentation of plant disease leaf images

Fuzzy-Bildverarbeitung

Some studies on fuzzy clustering of psychosis data

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