کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7375272 | 1480071 | 2018 | 20 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
GDPC: Gravitation-based Density Peaks Clustering algorithm
ترجمه فارسی عنوان
تولید ناخالص داخلی: الگوریتم خوشه بندی تراکم بر پایه گرانشی
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کلمات کلیدی
00-01، 99-00، تجزیه خوشه ای، خوشه کششی چگالی، تئوری گرانش تشخیص آنومالی،
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
فیزیک ریاضی
چکیده انگلیسی
The Density Peaks Clustering algorithm, which we refer to as DPC, is a novel and efficient density-based clustering approach, and it is published in Science in 2014. The DPC has advantages of discovering clusters with varying sizes and varying densities, but has some limitations of detecting the number of clusters and identifying anomalies. We develop an enhanced algorithm with an alternative decision graph based on gravitation theory and nearby distance to identify centroids and anomalies accurately. We apply our method to some UCI and synthetic data sets. We report comparative clustering performances using F-Measure and 2-dimensional vision. We also compare our method to other clustering algorithms, such as K-Means, Affinity Propagation (AP) and DPC. We present F-Measure scores and clustering accuracies of our GDPC algorithm compared to K-Means, AP and DPC on different data sets. We show that the GDPC has the superior performance in its capability of: (1) detecting the number of clusters obviously; (2) aggregating clusters with varying sizes, varying densities efficiently; (3) identifying anomalies accurately.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Physica A: Statistical Mechanics and its Applications - Volume 502, 15 July 2018, Pages 345-355
Journal: Physica A: Statistical Mechanics and its Applications - Volume 502, 15 July 2018, Pages 345-355
نویسندگان
Jianhua Jiang, Dehao Hao, Yujun Chen, Milan Parmar, Keqin Li,