کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4970131 | 1450027 | 2017 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
k-quantiles: L1 distance clustering under a sum constraint
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
This paper considers centroid-based clustering under â1 distance in which both data points and cluster centers are subject to a sum constraint on their components. A closed-form solution is derived for the cluster center optimization problem, enabling an interpretation as a sample quantile of the cluster. An adaptive sampling initialization step is also adopted to provide a guarantee on expected clustering cost as well as empirical improvements. Experiments on synthetic data indicate that the advantages of the proposed algorithms increase as clusters become more concentrated and as the dimension increases. An application to clustering employee job role profiles highlights the utility of â1 distance in promoting sparse, interpretable cluster centers.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 92, 1 June 2017, Pages 49-55
Journal: Pattern Recognition Letters - Volume 92, 1 June 2017, Pages 49-55
نویسندگان
Dennis Wei,