کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
533556 | 870133 | 2011 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Automatically finding clusters in normalized cuts
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
Normalized Cuts is a state-of-the-art spectral method for clustering. By applying spectral techniques, the data becomes easier to cluster and then k-means is classically used. Unfortunately the number of clusters must be manually set and it is very sensitive to initialization. Moreover, k-means tends to split large clusters, to merge small clusters, and to favor convex-shaped clusters. In this work we present a new clustering method which is parameterless, independent from the original data dimensionality and from the shape of the clusters. It only takes into account inter-point distances and it has no random steps. The combination of the proposed method with normalized cuts proved successful in our experiments.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 44, Issue 7, July 2011, Pages 1372–1386
Journal: Pattern Recognition - Volume 44, Issue 7, July 2011, Pages 1372–1386
نویسندگان
Mariano Tepper, Pablo Musé, Andrés Almansa, Marta Mejail,