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| Titel: |
Approximate minimum spanning tree clustering in high-dimensional space |
| Auteur: |
Lai, Chih Rafa, Taras Nelson, Dwight E. |
| Verschenen in: |
Intelligent data analysis |
| Paginering: | Jaargang 13 (2009) nr. 4 pagina's 575-597 |
| Jaar: |
2009-08-27 |
| Inhoud: |
Minimum spanning tree (MST) clustering sequentially inserts the nearest points in the R^{d} space into a list which is then divided into clusters by using desired criteria. This insertion order, however, can be relaxed provided approximately nearby points in a condensed area are adjacently inserted into a list before distant points in other areas. Based on this observation, we propose an approximate clustering method in which a new Approximate MST (AMST) is repeatedly built in the maximum (d+1) iterations from two sources: a new Hilbert curve created from carefully shifted N data points, and a previous AMST which holds cumulative vicinity information derived from earlier iterations. Although the final AMST may not completely match to a true MST built from an O(N^{2}) algorithm, most mismatches occur locally within individual data groups which are unimportant for clustering. Our experiments on synthetic datasets and animal motion vectors extracted from surveillance videos show that high-quality clusters can be efficiently obtained from this approximation method. |
| Uitgever: |
IOS Press |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
Details van artikel 3 van 9 gevonden artikelen
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