Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly
finer granularity. Motivated by the fact that most work on hierarchical clustering was based …

Hierarchical clustering is a data analysis method that has been used for decades. Despite its
widespread use, the method has an underdeveloped analytical foundation. Having a well …

Graph clustering is a fundamental task in many data-mining and machine-learning
pipelines. In particular, identifying a good hierarchical structure is at the same time a …

Clustering time series is a useful operation in its own right, and an important subroutine in
many higher-level data mining analyses, including data editing for classifiers …

Hierarchical clustering is typically performed using algorithmic-based optimization searching
over the discrete space of trees. While these optimization methods are often effective, their …

Many modern clustering methods scale well to a large number of data points, N, but not to a
large number of clusters, K. This paper introduces PERCH, a new non-greedy, incremental …

The applicability of agglomerative clustering, for inferring both hierarchical and flat
clustering, is limited by its scalability. Existing scalable hierarchical clustering methods …

This paper considers the problem of clustering a partially observed unweighted graph--ie,
one where for some node pairs we know there is an edge between them, for some others we …

We study the design of interactive clustering algorithms. The user supervision that we
consider is in the form of cluster split/merge requests; such feedback is easy for users to …

It is shown that the problem of evaluating an Nth degree polynomial is reducible to the
problem of dividing the polynomial. A method for dividing an Nth degree polynomial by an …