Abstract: Clustering aggregation can offer a partition that could better reflect the inherent structure of the data set by studying and integrating many clustering results of the original data set. Clustering aggregation could detect and deal with the isolated points preferably, which improves the quality of clustering. This paper made an overview of the relevant knowledge of the clustering aggregation, presented the concepts and advantages of clustering aggregation. It presented three approaches to get clustering members according to the ucsd cluster algorithms; analysed their respective advantages, disadvantages and application conditions; presented the existing consensus functions; explained the basic principles and pointed out their limitations. Finally, it discussed the future research directions.

Key words: Clustering aggregation, Clustering member, Consensus function, Clustering algorithms