
A Novel Clustering Algorithm Based Upon Games on Evolving Network
This paper introduces a model based upon games on an evolving network, a...
read it

How the result of graph clustering methods depends on the construction of the graph
We study the scenario of graphbased clustering algorithms such as spect...
read it

SameCluster Querying for Overlapping Clusters
Overlapping clusters are common in models of many practical datasegment...
read it

Measuring intercluster similarities with Alpha Shape TRIangulation in loCal Subspaces (ASTRICS) facilitates visualization and clustering of highdimensional data
Clustering and visualizing highdimensional (HD) data are important task...
read it

Learning Resolution Parameters for Graph Clustering
Finding clusters of wellconnected nodes in a graph is an extensively st...
read it

Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis
Several data analysis techniques employ similarity relationships between...
read it

Optimizing PID parameters with machine learning
This paper examines the Evolutionary programming (EP) method for optimiz...
read it
A numerical measure of the instability of Mappertype algorithms
Mapper is an unsupervised machine learning algorithm generalising the notion of clustering to obtain a geometric description of a dataset. The procedure splits the data into possibly overlapping bins which are then clustered. The output of the algorithm is a graph where nodes represent clusters and edges represent the sharing of data points between two clusters. However, several parameters must be selected before applying Mapper and the resulting graph may vary dramatically with the choice of parameters. We define an intrinsic notion of Mapper instability that measures the variability of the output as a function of the choice of parameters required to construct a Mapper output. Our results and discussion are general and apply to all Mappertype algorithms. We derive theoretical results that provide estimates for the instability and suggest practical ways to control it. We provide also experiments to illustrate our results and in particular we demonstrate that a reliable candidate Mapper output can be identified as a local minimum of instability regarded as a function of Mapper input parameters.
READ FULL TEXT
Comments
There are no comments yet.