… More specifically, we obtain the clustering of data by applying the K-means algorithm [11]
to the normalized rows of a matrix whose columns are the n bottom eigenvectors of the …

… We call these methods soft subspace clustering. In this paper, we present a new k-means
type algorithm for soft subspace clustering of large high-dimensional sparse data. We consider …

… Using a criterion with a lasso-type penalty, Sparse K-means set to zero the weights of the …
a new sparse subspace clustering method called SSKM (Sparse Subspace K-Means) that per…

… Segmentation of the trajectories follows by applying K-means to the n ∈ {2, 3} eigenvectors
of L corresponding to the smallest n eigenvalues. Figure 1 shows the adjacency matrices for …

… methods: K-means, reduced K-means, factorial K-means, mixtures … similar to that of subspace
K-means in easy conditions but … , we show that subspace K-means analysis provides a rich …

… Therefore, a clustering algorithm such as K-means can be … a novel kernel sparse subspace
clustering algorithm which … manifold, we seek its sparse representation via exploiting the …

… computing the subspace structured sparse representation … set Q by applying the k-means
algorithm. We call this procedure … Therefore, instead of quantizing Q by using k-means in each …

… The partition-based clustering algorithms, like KMeans and fuzzy K-Means, are most widely
… and sparse fuzzy K-Means clustering algorithm, an extension to the standard fuzzy K-Means …

… In the same way, we got another nine data sets for KASP and LSC K by performing k-means
to select p in-sample data points. To avoid the difference in data set, we ran different …

… Abstract—Sparse subspace clustering (SSC) … subspaces to the data center, which then
conducts subspace information fusion using a k-means type method; the aggregated subspace …