Code covered by the BSD License
- XieBeniInverted(U, uExpoe...XIEBENIINVERTED Implementation of the measure of cluster validation of the Xie-Beni.
- distfcmfp(center, data)DISTFCMFP Distance measure in fuzzy c-mean clustering with focal point.
- fcmfp(data, cluster_n, op...FCMFP Data set clustering using fuzzy c-means clusteringwith focal point.
- fcmfpdemo(varargin)FCMFPDEMO MATLAB code for fcmfpdemo.fig
- iterfcmfp(handles, data)ITERFCMFP Implementation of the Fuzzy C-Means algorithm with Focal Point.
- rectifyPoints(CENTER, foc...RECTIFYPOINTS Projection of the Centers for Z = 0, using the vector equation of the line.
- stepfcmfp(data, U, cluste...STEPFCMFP One step in fuzzy c-mean clustering.
- updateGraphic(data, hObje...UPDATEGRAPHIC Draw the data in the GUI.
- updateU(data, cluster_n, ...UPDATEU Updating the partition matrix U.
- verifyInputs(data, handles)VERIFYINPUTS Verify the values entered in the GUI, in order to validate if they are valid.
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Fuzzy C-Means with Focal Point
01 Sep 2011
We present a generalization of partitional clustering.
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In this work we present a generalization of partitional clustering aiming at the inclusion into the clustering process of both distance and direction of the point of observation towards the dataset. This is done by incorporating a new term in the objective function, accounting for the distance between the clusters’ prototypes and the point of observation. It is a well known fact that the chosen number of partitions has a major effect on the objective function based partitional clustering algorithms, conditioning both the level of granularity of the data grouping and the capability of the algorithm to accurately reflect the underlying structure of the data.
| Required Products
Fuzzy Logic Toolbox
MATLAB 7.12 (R2011a)