A Fixed point view: A Model-Based Clustering Framework

However, not all of the data are representative and meaningful, so the analysis and disposal of large-scale data occupies an increasingly important position in scientific research and social life [1]. Cluster analysis is an important unsupervised learning method in machine learning. Its basic idea is grouping a set of objects into clusters, in a way that objects in the same cluster share more similarity than those from separated clusters, in terms of distances of a certain space. In the evolution of clustering, due to the differences of data types and clustering strategies, cluster analysis can be divided into two main branches, namely, traditional clustering algorithms and modern clustering algorithms. Traditional clustering algorithms include clustering algorithm based on partition, density, model, fuzzy theory and so on [2, 3].