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

Ruoyu Zhou, Lianjie Shu and Yan Su

Computational Statistics & Data Analysis, 2015, vol. 89, issue C, 134-146

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.

Keywords: Minimum spanning tree; Spatial cluster detection; Arbitrary shape; Validity index (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947315000766
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:89:y:2015:i:c:p:134-146

DOI: 10.1016/j.csda.2015.03.008

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().