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TreeKDE: clustering multivariate data based on decision tree and using one-dimensional kernel density estimation.

D Scaldelai1, L C Matioli2, S R Santos1

  • 1Colegiado de Matemática, Universidade Estadual do Paraná-UNESPAR, Campo Mourão, Brazil.

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Summary
This summary is machine-generated.

We introduce TreeKDE, a novel algorithm for multidimensional data clustering. This method efficiently identifies data clusters and their boundaries using decision trees and kernel density estimation.

Keywords:
Decision treeGaussian kernelclustering datakernel density estimationoptimization method

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Clustering multidimensional data is a fundamental task in data analysis.
  • Existing algorithms may struggle with automatic cluster number determination and defining cluster boundaries.
  • There is a need for efficient and robust clustering methods.

Purpose of the Study:

  • To present a new algorithm, TreeKDE, for clustering multidimensional data.
  • To demonstrate the algorithm's capability for automatic cluster number determination.
  • To showcase its efficiency and competitiveness against existing methods.

Main Methods:

  • The TreeKDE algorithm utilizes a decision tree structure.
  • It optimizes a one-dimensional kernel density estimator (KDE).
  • Orthogonal projections of data onto coordinate axes are employed.

Main Results:

  • TreeKDE automatically determines the number of clusters.
  • It effectively defines cluster boundaries within rectangular regions.
  • Comparative experiments show TreeKDE is efficient and competitive.

Conclusions:

  • TreeKDE offers a simple and efficient approach to data clustering.
  • The algorithm shows promise for further research and development.
  • It provides a foundation for new clustering algorithms combining decision trees and KDE.