Sequential Hierarchical Pattern Clustering

Abstract

Abstract. Clustering is a widely used unsupervised data analysis technique in machine learning. However, a common requirement amongst many existing clustering methods is that all pairwise distances between patterns must be computed in advance. This makes it computationally expensive and difficult to cope with large scale data used in several applications, such as in bioinformatics. In this paper we propose a novel sequential hierarchical clustering technique that initially builds a hierarchical tree from a small fraction of the entire data, while the remaining data is processed sequentially and the tree adapted constructively. Preliminary results using this approach show that the quality of the clusters obtained does not degrade while reducing the computational needs.

Computational intelligence for predictive condition monitoring and approaches for online analysis

“…In the former, a datum can only belong to one cluster, but in fuzzy clustering methods, a datum can be a member of several clusters with a certain membership value. The process of assigning a datum to a cluster or some clusters depends on its distance, similarity, or connectivity to other data in that specific cluster [Farran et al, 2009, Berkhin, 2006.…”