MOTIVATION It is far from trivial to select the most effective clustering method and its parameterization, for a particular set of gene expression data, because there are a very large number of possibilities. Although many researchers still prefer to use hierarchical clustering in one form or another, this is often sub-optimal. Cluster ensemble research solves this problem by automatically comb...

Clustering algorithms strive to organize data into meaningful groups in an unsupervised fashion. For some datasets, these algorithms can provide important insights into the structure of the data and the relationships between the constituent items. Clustering analysis is applied in numerous fields, e.g., biology, economics, and computer vision. If the structure of the data changes over time, we ...

Clustering algorithm based on grid and density has many excellent features. But for the highdimensional data stream, the number of grids will be increased sharply as the space dimensionality grows. To solve the defect, we propose GDH-Stream, a clustering method based on the effective dimension and grid density for high-dimensional data stream, which consists of an online component and an offlin...

In this work, we address the problem of large-scale online face clustering: given a continuous stream unknown faces, create database grouping incoming faces by their identity. The must be updated every time new arrives. addition, solution efficient, accurate and scalable. For purpose, present an gaussian mixture-based clustering method (OGMC). key idea is proposal that identity can represented ...