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An adaptive minimum spanning tree test for detecting irregularly-shaped spatial clusters

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  • Zhou, Ruoyu
  • Shu, Lianjie
  • Su, Yan

Abstract

The clustering methodologies based on minimum spanning tree (MST) have been widely discussed due to their simplicity and efficiency in signaling irregular clusters. However, most of the MST-based clustering methods estimate the most likely cluster based on the maximum likelihood ratio from the resulting subtrees after the removal of edges of the MST. They can only estimate one cluster even if there are multiple clusters actually present over the study region. To overcome this limitation, we propose an adaptive MST (AMST) method to detect irregularly-shaped clusters. The basic idea is to first determine the best number of partition over the study region using a validity index and then to determine the significance of the candidate clusters. The comparison results with both the static and dynamic MST methods favor the proposed method.

Suggested Citation

  • Zhou, Ruoyu & Shu, Lianjie & Su, Yan, 2015. "An adaptive minimum spanning tree test for detecting irregularly-shaped spatial clusters," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 134-146.
    • Handle: RePEc:eee:csdana:v:89:y:2015:i:c:p:134-146
    DOI: 10.1016/j.csda.2015.03.008
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    References listed on IDEAS

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    4. Julian Besag & James Newell, 1991. "The Detection of Clusters in Rare Diseases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 154(1), pages 143-155, January.
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    Cited by:

    1. Ali Abolhassani & Marcos O. Prates & Safieh Mahmoodi, 2023. "Irregular Shaped Small Nodule Detection Using a Robust Scan Statistic," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 141-162, April.
    2. Self, Stella & Nolan, Melissa, 2024. "A Bayesian spatial scan statistic for multinomial data," Statistics & Probability Letters, Elsevier, vol. 206(C).

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