Clustering by Hill-Climbing: Consistency Results

02/18/2022
by   Ery Arias-Castro, et al.
0

We consider several hill-climbing approaches to clustering as formulated by Fukunaga and Hostetler in the 1970's. We study both continuous-space and discrete-space (i.e., medoid) variants and establish their consistency.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/15/2018

Morse Theory and an Impossibility Theorem for Graph Clustering

Kleinberg introduced three natural clustering properties, or axioms, and...
research
02/27/2022

Strong Consistency for a Class of Adaptive Clustering Procedures

We introduce a class of clustering procedures which includes k-means and...
research
08/27/2022

Consistency between ordering and clustering methods for graphs

A relational dataset is often analyzed by optimally assigning a label to...
research
04/13/2018

Clustering Analysis on Locally Asymptotically Self-similar Processes

In this paper, we design algorithms for clustering locally asymptoticall...
research
05/04/2021

Representation Learning for Clustering via Building Consensus

In this paper, we focus on deep clustering and unsupervised representati...
research
12/18/2015

Asymptotic Behavior of Mean Partitions in Consensus Clustering

Although consistency is a minimum requirement of any estimator, little i...
research
02/12/2022

Towards Continuous Consistency Axiom

Development of new algorithms in the area of machine learning, especiall...

Please sign up or login with your details

Forgot password? Click here to reset