It starts off by foregrounding disparate and clustered objects that have been scattered around various places in the disciplinary field, a presence of things made perceptible as they are subject to negotiation and contested claims.
The video depicts clustered objects in space, be they galaxies or clouds of miscellaneous debris.
A popular pixel-based segmentation method, which has proved to be useful on separating clustered objects, is the so-called watershed algorithm [18], [19].
Therefore a second step was taken to divide the clustered objects using a watershed segmentation[19], and all cells bordering the edge of images were discarded.
For each clustering object data Z i (i = 1,..., N), a feature vector that corresponds to a point in a D-dimensional space, we computed the distance: d ij = | Z i - Z j |, (i, j = 1,..., N).
More specifically, the smooth subspaces are represented by weighted time stamps which indicate the relative discriminative power of these time stamps for clustering objects.
The objective of this study was to simplify the cytometry datasets by arranging or clustering "objects" (cells) into a smaller number of relatively homogeneous groups (clusters) on the basis of interobject similarities and dissimilarities.
Ordinary one-way clustering algorithms cluster objects using the complete feature space, e.g. a clustering of the genes with respect to the gene expression values of all patients.
Correlation clustering, originally proposed by [ 17] and analyzed in [ 18- 20], is a method for clustering objects that are indirectly described by means of their mutual relationships.
Each object is then characterized by a small set of object features, and objects are clustered into groups (object types).
