Temat: fuzzy c-means clustering - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: An ε-Insensitive Approach to Fuzzy Clustering
Autorzy:: Łęski, J.
Powiązania:: https://bibliotekanauki.pl/articles/908067.pdf
Data publikacji:: 2001
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: programowanie
metoda grupowania
fuzzy clustering
fuzzy c-means
robust methods
varepsilon-insensitivity
fuzzy c-medians
Opis:: Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in the data. The present paper introduces a new varepsilon-insensitive Fuzzy C-Means (varepsilonFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). The performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) method using synthetic data with outliers and heavy-tailed, overlapped groups of the data.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2001, 11, 4; 993-1007
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: An automatic segmentation method for scanned images of wheat root systems with dark discolourations
Autorzy:: Gocławski, J.
Sekulska-Nalewajko, J.
Gajewska, E.
Wielanek, M.
Powiązania:: https://bibliotekanauki.pl/articles/930018.pdf
Data publikacji:: 2009
Wydawca:: Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:: system korzeniowy
segmentacja
szkielet
klasteryzacja
root system image
segmentation
skeleton
root discolourations
fuzzy c-means clustering
Opis:: The analysis of plant root system images plays an important role in the diagnosis of plant health state, the detection of possible diseases and growth distortions. This paper describes an initial stage of automatic analysis-the segmentation method for scanned images of Ni-treated wheat roots from hydroponic culture. The main roots of a wheat fibrous system are placed separately in the scanner view area on a high chroma background (blue or red). The first stage of the method includes the transformation of a scanned RGB image into the HCI (Hue-Chroma-Intensity) colour space and then local thresholding of the chroma component to extract a binary root image. Possible chromatic discolourations, different from background colour, are added to the roots from blue or red chroma subcomponent images after thresholding. At the second stage, dark discolourations are extracted by local fuzzy c-means clustering of an HCI intensity image within the binary root mask. Fuzzy clustering is applied in local windows around the series of sample points on roots medial axes (skeleton). The performance of the proposed method is compared with hand-labelled segmentation for a series of several root systems.
Źródło:: International Journal of Applied Mathematics and Computer Science; 2009, 19, 4; 679-689
1641-876X
2083-8492
Pojawia się w:: International Journal of Applied Mathematics and Computer Science
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Fast FCM with spatial neighborhood information for brain MR image segmentation
Autorzy:: Biniaz, A.
Abbasi, A.
Powiązania:: https://bibliotekanauki.pl/articles/91616.pdf
Data publikacji:: 2013
Wydawca:: Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:: Fuzzy c-Means clustering
FCM
Fast FCM
FFCM
spatial Fast FCM
sFFCM
MR image
noise interference
Opis:: Among different segmentation approaches Fuzzy c-Means clustering (FCM) is a welldeveloped algorithm for medical image segmentation. In emergency medical applications quick convergence of FCM is necessary. On the other hand spatial information is seldom exploited in standard FCM; therefore nuisance factors can simply affect it and cause misclassification. This paper aims to introduce a Fast FCM (FFCM) technique by incorporation of spatial neighborhood information which is exploited by a linear function on fuzzy membership. Applying proposed spatial Fast FCM (sFFCM), elapsed time is decreased and neighborhood spatial information is exploited in FFCM. Moreover, iteration numbers by proposed FFCM/sFFCM techniques are decreased efficiently. The FCM/FFCM techniques are examined on both simulated and real MR images. Furthermore, to considerably decrease of convergence time and iterations number, cluster centroids are initialized by an algorithm. Accuracy of the new approach is same as standard FCM. The quantitative assessments of presented FCM/FFCM techniques are evaluated by conventional validity functions. Experimental results demonstrate that sFFCM techniques efficiently handle noise interference and significantly decrease elapsed time.
Źródło:: Journal of Artificial Intelligence and Soft Computing Research; 2013, 3, 1; 15-25
2083-2567
2449-6499
Pojawia się w:: Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:: Biblioteka Nauki

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