Przykłady cluster

• We also evaluate the stability of clusters with respect to synthetic remeasured array data.

• Even with the model-based algorithms, the number of clusters can only be estimated.

• The steps of assigning genes to centroids and computing new centroids are repeated until no genes are moved between clusters.

• Hence, it is of interest to study the performance of various clustering approaches as a function of noise level in the synthetic data.

• Clustering with repeated measurements produced more accurate clusters than clustering with the estimated true mean data in most cases.

• In a data matrix, rows usually represent objects to be clustered (typically genes), and columns usually represent features or attributes of the objects (typically experiments).

• In each step, similar subtrees (clusters) are merged to form the dendrogram.

• Assuming known categories (classes) of objects are available, we can compare clustering results by assessing the agreement of the clusters with the classes.

• The variants are not interspersed with one another; but form segregated clusters.

• Finally, there are a variety of clustering and data-mining techniques for understanding the structure of the data in other ways.

• At present the model only contains a limited framework supporting node- and tree-based clusters.

• We will then examine whether the resulting clusters are associated with cohort status and relevant sociodemographic variables.

• Is there a group difference in the number and size of clusters produced?

• We can overcome the problem of data sparseness by applying not co-occurring words but co-occurring clusters to the similarity of target words.

• The exemplar clusters are embedded in a network of associations among words that map relations of similarity at all levels.