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Hierarchical clustering
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 …
binary merge tree starting from leaves that contain data elements to the root that contains the …
To cluster, or not to cluster: An analysis of clusterability methods
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 …
data. For most applications, applying clustering is only appropriate when cluster structure is …
Hierarchical clustering: Objective functions and algorithms
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 …
finer granularity. Motivated by the fact that most work on hierarchical clustering was based …
Beyond worst-case analysis
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 …
VOL. 62 | NO. 3 review articles COMPARING DIFFERENT ALGORITHMS is hard. For almost …
Local-pattern-aware forecast of regional wind power: Adaptive partition and long-short-term matching
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 …
emerging. As influenced by the geographical features of the wind region, the power output …
On learning mixtures of well-separated gaussians
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 …
Gaussians, when the components of the mixture are well separated. In the most basic form …
Partitioning well-clustered graphs: Spectral clustering works!
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 …
graph into k clusters via (1) embedding the vertices of a graph into a low-dimensional space …
Clustering with same-cluster queries
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 …
the learner is allowed to interact with a domain expert, asking whether two given instances …
Approximate clustering without the approximation
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 …
research, with much research effort spent on getting a better understanding of the …
Clustering under perturbation resilience
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 …
instances are often based on heuristic measures, Bilu and Linial Proceedings of the …