Hierarchical density-based clustering is a powerful tool for exploratory data analysis, which can play an important role in the understanding and organization of datasets. However, its applicability to large datasets limited because computational complexity hierarchical methods has quadratic lower bound number objects be clustered. MapReduce popular programming model speed up mining machine lea...

We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally, we describe a recently developed very efficient (linear time) hierarchical clusterin...

Three important generalizations of the basic clustering problem are relational, hierarchical, and multiple clustering. This paper proposes the first approach to clustering that unifies all three. We describe a general probabilistic model for relational clustering, and show that flat, hierarchical and multiple relational clustering models are special cases. This paper also describes an efficient...

Organizing data into hierarchies is natural for humans. However, there is little work in machine learning that explores human-machine mixed-initiative approaches to organizing data into hierarchical clusters. In this paper we consider mixed-initiative clustering of a user’s email, in which the machine produces (initial and retrained) hierarchical clusterings of email, and the user reviews and e...

Adaptive tree structured clustering (ATSC) is our proposed divisive hierarchical clustering method that recursively divides a data set into 2 subsets using self-organizing feature map (SOM). In each partition, the data set is quantized by SOM and the quantized data is divided using agglomerative hierarchical clustering. ATSC can divide data sets regardless of data size in feasible time. On the ...