The Shape Metric for Clustering Algorithms

07/26/2017

∙

We construct a method by which we can calculate the precision with which an algorithm identifies the shape of a cluster. We present our results for several well known clustering algorithms and suggest ways to improve performance for newer algorithms.

READ FULL TEXT

The Shape Metric for Clustering Algorithms

Implementation of Fuzzy C-Means and Possibilistic C-Means Clustering Algorithms, Cluster Tendency Analysis and Cluster Validation

A new selection strategy for selective cluster ensemble based on Diversity and Independency

Fast Combinatorial Algorithms for Min Max Correlation Clustering

Supervised Enhanced Soft Subspace Clustering (SESSC) for TSK Fuzzy Classifiers

Intrinsic Hierarchical Clustering Behavior Recovers Higher Dimensional Shape Information

A Framework for Benchmarking Clustering Algorithms

The Shape Metric for Clustering Algorithms

Related Research

Implementation of Fuzzy C-Means and Possibilistic C-Means Clustering Algorithms, Cluster Tendency Analysis and Cluster Validation

A new selection strategy for selective cluster ensemble based on Diversity and Independency

Fast Combinatorial Algorithms for Min Max Correlation Clustering

Supervised Enhanced Soft Subspace Clustering (SESSC) for TSK Fuzzy Classifiers

Intrinsic Hierarchical Clustering Behavior Recovers Higher Dimensional Shape Information

A Framework for Benchmarking Clustering Algorithms