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A General Iterative Clustering Algorithm.

Ziqiang Lin1, Eugene Laska1,2, Carole Siegel1,2

  • 1Department of Psychiatry, NYU Langone School of Medicine, New York, NY, USA.

Statistical Analysis and Data Mining
|September 5, 2022
PubMed
Summary
This summary is machine-generated.

The General Iterative Cluster (GIC) algorithm enhances cluster analysis by iteratively improving dissimilarity measures using random forest proximity matrices. This data-driven approach significantly boosts clustering performance compared to standard methods.

Keywords:
ClusteringExtremely Randomized TreeExtremely randomized treeProximityRandom Forestiterative RF clustering

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Area of Science:

  • Machine Learning
  • Data Mining
  • Bioinformatics

Background:

  • Cluster analysis quality relies on accurate dissimilarity measures.
  • Data-driven dissimilarity is more objective than geometric measures like Euclidean distance.
  • Decision tree ensembles, such as random forests (RF), generate proximity matrices for dissimilarity calculations.

Purpose of the Study:

  • Introduce the General Iterative Cluster (GIC) algorithm for improved cluster analysis.
  • Enhance the proximity matrix and clustering results from base random forest models.
  • Provide an iterative method for refining cluster assignments.

Main Methods:

  • Utilize random forest (RF) ensembles to generate proximity matrices from labeled real and synthetic data.
  • Transform RF proximity matrices into dissimilarity matrices for input into clustering algorithms.
  • Iteratively refine RF models and cluster assignments until convergence, forming the GIC algorithm.
  • Apply the GIC procedure with base procedures like Extremely Randomized Trees.

Main Results:

  • The GIC algorithm substantially improves clustering performance, as measured by the Silhouette Score.
  • Evaluated performance on benchmark and simulated datasets demonstrates superior results compared to base clustering.
  • The iterative refinement process leads to more robust and accurate cluster assignments.

Conclusions:

  • The General Iterative Cluster (GIC) algorithm offers a significant advancement in unsupervised learning and cluster analysis.
  • Data-driven dissimilarity measures derived from ensemble methods provide more objective clustering inputs.
  • The GIC algorithm is available as an R package for practical application in various scientific domains.