The historical data of load, such as wind power data, are handled by using a hierarchical clustering algorithm and principal component feature extraction technology.
The simulated temporal profiles are grouped into c clusters (Fig. 1 C) using a feature-based k -mean clustering algorithm.
Since we have no training data available for the classification process, the general idea of the methods is to split the classification/labeling procedure into two main steps: in a local clustering phase each node calculates a preliminary estimate of the cluster characteristics (i.e., centroids and covariances) of each cluster using a small number of feature vectors.
Next, we evaluate the SPEX features on clustering, using a popular (but not necessarily optimal) clustering algorithm, the spectral clustering (SC).
The modified hyperplane clustering algorithm (HPCluster) works in two stages: (i) reduction of the dataset using cluster features (CFs) and (ii) conventional k-means clustering on the CFs obtained in stage 1.
Then, the feature points are clustered using a Bayesian framework, under the assumption that pairs of points belonging to a same person have a small variance in their mutual distance (quasi-rigid motion).
Most feature recognition methods use either a cluster-based decomposition or feature line extraction through solid angles or curvature values, followed by graph-based heuristics.
To dissect the nature of the pathogen-associated proteins we next used an unsupervised clustering method based on 35 features (e.g. signal peptide prediction score, molecular weight, amino acid composition), deduced from the sequences.
We presented a study in which we compared some of the common used classification, clustering, and feature selection methods.
In this paper, we have attempted to use attribute clustering for feature selection.
