Agglomerative hierarchical clustering differs from partition-based clustering since it builds a
binary merge tree starting from leaves that contain data elements to the root that contains the …

Beyond worst-case analysis Page 1 88 COMMUNICATIONS OF THE ACM | MARCH 2019 |
VOL. 62 | NO. 3 review articles COMPARING DIFFERENT ALGORITHMS is hard. For almost …

Clustering is an essential data mining tool that aims to discover inherent cluster structure in
data. For most applications, applying clustering is only appropriate when cluster structure is …

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 …

Importance for the accurate forecast of wind region with multiple wind farms is gradually
emerging. As influenced by the geographical features of the wind region, the power output …

We consider the problem of efficiently learning mixtures of a large number of spherical
Gaussians, when the components of the mixture are well separated. In the most basic form …

In this work we study the widely used\emphspectral clustering algorithms, ie partition a
graph into k clusters via (1) embedding the vertices of a graph into a low-dimensional space …

We propose a framework for Semi-Supervised Active Clustering framework (SSAC), where
the learner is allowed to interact with a domain expert, asking whether two given instances …

Approximation algorithms for clustering points in metric spaces is a flourishing area of
research, with much research effort spent on getting a better understanding of the …

Motivated by the fact that distances between data points in many real-world clustering
instances are often based on heuristic measures, Bilu and Linial Proceedings of the …