OnlineGradedPossibi​​listicClustering

Versione 1.3 (1,24 MB) da Amr Abdullatif
Implementation of Online Graded Possibilistic Clustering "OGPC" Clustering in MATLAB
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Aggiornato 1 ott 2017

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Implementation of Online Graded Possibilistic Clustering "OGPC" Clustering in MATLAB.
An algorithm that is able to detect outliers and adapt to concept shift drift in data streams.
Read more about the online algorithm "https://www.springerprofessional.de/graded-possibilistic-clustering-of-non-stationary-data-streams/12047532.
Notes:
- This a modified version of the online algorithm (Algorithm able to detect outliers and adapt to concept shift).
- The algorithm can work on raw (But you will have to change dynamic plot axis limit to visualize the clusters centroids) and on normalized data (Apply normalizer on the training set).
Developer: Amr Abdullatif (DIBRIS-University of Genoa)
Publisher: DIBRIS (www.dibris.unige.it)
Contact Info: amr.r.abdullatif@gmail.com
Function:
[rhovals,summembership,U, Youtn, Y, normvals, bend, uval, yval] = ogpc(X, Y, fb, K, maxiter, eta0, alphamin, plt)

Cita come

Amr Abdullatif (2025). OnlineGradedPossibi​listicClustering (https://it.mathworks.com/matlabcentral/fileexchange/64318-onlinegradedpossibi-listicclustering), MATLAB Central File Exchange. Recuperato .

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Creato con R2017a
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Versione Pubblicato Note della release
1.3

Change in
- learning region parameters (Concept drift "slow learn", outliers "no learn", Concept shift "fast learn").
- Cluster width update function.
1.2.0.0

Change
Update beta inside ogpc.m
" bi0 = bend ; "
1.1.0.0

Modify title and description.

Update description.
Update description structure.