کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4969542 | 1449976 | 2017 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Fast density clustering strategies based on the k-means algorithm
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
Clustering by fast search and find of density peaks (CFSFDP) is a state-of-the-art density-based clustering algorithm that can effectively find clusters with arbitrary shapes. However, it requires to calculate the distances between all the points in a data set to determine the density and separation of each point. Consequently, its computational cost is extremely high in the case of large-scale data sets. In this study, we investigate the application of the k-means algorithm, which is a fast clustering technique, to enhance the scalability of the CFSFDP algorithm while maintaining its clustering results as far as possible. Toward this end, we propose two strategies. First, based on concept approximation, an acceleration algorithm (CFSFDP+A) involving fewer distance calculations is proposed to obtain the same clustering results as those of the original algorithm. Second, to further expand the scalability of the original algorithm, an approximate algorithm (CFSFDP+DE) based on exemplar clustering is proposed to rapidly obtain approximate clustering results of the original algorithm. Finally, experiments are conducted to illustrate the effectiveness and scalability of the proposed algorithms on several synthetic and real data sets.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 71, November 2017, Pages 375-386
Journal: Pattern Recognition - Volume 71, November 2017, Pages 375-386
نویسندگان
Liang Bai, Xueqi Cheng, Jiye Liang, Huawei Shen, Yike Guo,