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K-means performs the clustering by minimizing the distances of the vectors to their cluster prototype.
If any distance is smaller than the distance of the corresponding vector to its own cluster prototype, it is evidence that k-means has potential to operate between the clusters.
The proposed hybrid system is designed and tested in a real environment, with an event filter cluster prototype based on the architecture of the Compact Muon Solenoid experiment at CERN.
Due to this re-weighting, all points in the vicinity of the chosen cluster prototype receive a larger γ-index.
The lowest γ-index indicates that the corresponding filter is inside a region with many other filter examples and should therefore be chosen as cluster prototype.
To ensure that we also sample prototypes from other clusters, an incremental procedure of choosing and re-weighting is applied to determine a predefined number of cluster prototype filters.
Similar(52)
First one is to compute cluster prototypes, v i for each cluster i. Cluster prototypes refer to the average values of features of member objects (real data, z k ), weighted by membership degree of the object.
The classical K-means algorithm initially chooses random cluster prototypes according to a user-defined selection process.
They modified the objective function so that the FCM algorithm worked on distances to the Voronoi cell of the cluster rather than using distances to the cluster prototypes.
Missing values are processed within a general fuzzy min max neural network architecture utilising hyperbox fuzzy sets as input data cluster prototypes.
where ||z k - v i ||2 is the chosen distance measure between a data point z k and the cluster v i is an indicator of the distance of the data points from their cluster prototypes.
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