Adaptive fuzzy clustering by fast search and find of density peaks
Adaptive fuzzy clustering by fast search and find of density peaks
复制标题
通过快速搜索和发现密度峰值进行自适应模糊聚类
DOI：
10.1007/s00779-016-0954-4
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发表时间：
2016-10-01
影响因子：
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通讯作者：
Dawood, Hussain
中科院分区：
文献类型：
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作者：
Bie, Rongfang;Mehmood, Rashid;Dawood, Hussain
提出了通过快速搜索和查找密度峰值进行聚类（CFSFDP），通过查找密度峰值来对数据进行聚类。 CFSFDP 基于两个假设：与周围邻居相比，聚类中心是高密度数据点，并且与其他聚类中心距离较远。基于这些假设，CFSFDP 支持一种启发式方法（称为决策图）来手动选择聚类中心。手动选择聚类中心是CFSFDP在智能数据分析中的一大局限性。在本文中，我们提出了一种模糊CFSFDP方法来有效地自适应选择聚类中心。它使用模糊规则，基于上述假设来选择聚类中心。我们对九个合成聚类数据集进行了大量实验，并将所得聚类与最先进的方法进行了比较。聚类结果和合成数据的比较验证了所提出的模糊CFSFDP方法的鲁棒性和有效性。
Clustering by fast search and find of density peaks (CFSFDP) is proposed to cluster the data by finding of density peaks. CFSFDP is based on two assumptions that: a cluster center is a high dense data point as compared to its surrounding neighbors, and it lies at a large distance from other cluster centers. Based on these assumptions, CFSFDP supports a heuristic approach, known as decision graph to manually select cluster centers. Manual selection of cluster centers is a big limitation of CFSFDP in intelligent data analysis. In this paper, we proposed a fuzzy-CFSFDP method for adaptively selecting the cluster centers, effectively. It uses the fuzzy rules, based on aforementioned assumption for the selection of cluster centers. We performed a number of experiments on nine synthetic clustering datasets and compared the resulting clusters with the state-of-the-art methods. Clustering results and the comparisons of synthetic data validate the robustness and effectiveness of proposed fuzzy-CFSFDP method.