Research On Clustering Ensemble And Its Applications

With the development of human society, we will face the problem of information explosion in the future. In this context, the technology of cluster analysis has been vigorous development.People have proposed a lot of clustering algorithms. However, any clustering algorithm has a assumption about data distribution, resulting in that a single clustering algorithm can not achieve satisfactory result in many cases. Clustering ensemble algorithm can be more advantageous than a single algorithm.Clustering ensemble algorithms have been successfully applied in many areas and are considered as one of the four important future research directions for machine learning.The main content of this thesis is about the clustering ensemble. First, using the concept of nearest neighbor, this thesis proposed a new clustering ensemble algorithm-ANNCE.The advantages of this algorithm are:First, it combin the results of multiple clustering algorithms and has better stability and accuracy; Secondly, according to the different density of data, our algorithms can effectively automatically select a different number of nearest neighbors for each data point and get a good result. IN the second part of this thesis, we propose two concepts-"absolute core cluster"(ACC) and "relative core cluster"(RCC), and we proposed RCBK-means (Relative Core Cluster Based K-means) algorithm to solve the local optimization problem. In addition, we also used RCC to improve result of the CBEC algorithm and.proposed RCC-CBEC algorithm.