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一種基于密度的模糊自適應聚類算法

A density-based fuzzy adaptive clustering algorithm

  • 摘要: 針對密度聚類算法對鄰域參數設置敏感的問題,提出一種基于密度的模糊自適應聚類算法.算法在無需預先設置聚類數以及鄰域參數的情況下,可以自適應地根據樣本間距離關系確定鄰域半徑得到樣本密度,并根據樣本密度逐漸增加聚類中心.為了保障聚類結果的正確性,同時提出一種新的模糊聚類有效性指標以判斷最佳聚類數,消除了密度聚類算法對參數的敏感性.用UCI基準數據集進行實驗,發現本文算法在對數據進行聚類時,聚類質量較原始密度聚類算法在準確性和自適應性方面均有顯著提高.

     

    Abstract: In order to solve the problem that the density clustering algorithm is sensitive to neighborhood parameters, this article introduces a density-based fuzzy adaptive clustering algorithm. Without predefined clustering number and neighborhood parameters, this algorithm adaptively determines the radius of neighborhood to obtain the density of each sample and increases cluster centers based on the density. A new validity measure for fuzzy clustering is proposed to choose the best clustering number so that the sensitivity of density clustering is eliminated. UCI benchmark data sets are used to compare the proposed algorithm and the traditional density clustering algorithm. Experiment results demonstrate that the proposed algorithm improves the clustering accuracy and the adaptability effectively.

     

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