A popular unsupervised learning method, known as clustering, is extensively used in data
mining, machine learning and pattern recognition. The procedure involves grouping of …

Big data analytics is one of the emerging technologies as it promises to provide better
insights from huge and heterogeneous data. Big data analytics involves selecting the …

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 …

Cluster ensemble has emerged as a powerful technique for combining multiple clustering
results. To address the problem of clustering on large-scale data, this paper presents an …

Appropriately handling the scalability of clustering is a long-standing challenge for the study
of clustering techniques and is of fundamental interest to researchers in the community of …

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

We present first massively parallel (MPC) algorithms and hardness of approximation results
for computing Single-Linkage Clustering of $ n $ input $ d $-dimensional vectors under …

Clouds play an important role in the Earth's energy budget, and their behavior is one of the
largest uncertainties in future climate projections. Satellite observations should help in …

We introduce TeraHAC, a (1+ ε)-approximate hierarchical agglomerative clustering (HAC)
algorithm which scales to trillion-edge graphs. Our algorithm is based on a new approach to …

Clustering is one of the most important unsupervised machine learning tasks, which is
widely used in information retrieval, social network analysis, image processing, and other …