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Probability density estimation using a Gaussian clustering algorithm

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Abstract

A version of the Tråvén's [1] Gaussian clustering algorithm for normal mixture densities is studied. Unlike in the case of the Tråvén's algorithm, no constraints on covariance structure of mixture components are imposed. Simulations suggest that the modified algorithm is a very promising method of estimating arbitrary continuous d-dimensional densities. In particular, the simulations have shown that the algorithm is robust against assuming the initial number of mixture components to be too large.

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Authors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, Ordona 21, 01-237, Warsaw, Poland

    J. Ćwik & J. Koronacki

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  1. J. Ćwik
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  2. J. Koronacki
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Additional information

This work was supported in part by the State Committee for Scientific Research (KBN) under grant PB 0589/P3/94/06. It was completed while the second author was on leave to the Department of Statistics, Rice University, Houston, Texas.

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Ćwik, J., Koronacki, J. Probability density estimation using a Gaussian clustering algorithm. Neural Comput & Applic 4, 149–160 (1996). https://doi.org/10.1007/BF01414875

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