CAJ | 학술논문

针对密度聚类算法对邻域参数设置敏感的问题，提出一种基于密度的模糊自适应聚类算法。算法在无需预先设置聚类数以及邻域参数的情况下，可以自适应地根据样本间距离关系确定邻域半径得到样本密度，并根据样本密度逐渐增加聚类中心。为了保障聚类结果的正确性，同时提出一种新的模糊聚类有效性指标以判断最佳聚类数，消除了密度聚类算法对参数的敏感性。用 UCI 基准数据集进行实验，发现本文算法在对数据进行聚类时，聚类质量较原始密度聚类算法在准确性和自适应性方面均有显著提高。
침대밀도취류산법대린역삼수설치민감적문제，제출일충기우밀도적모호자괄응취류산법。산법재무수예선설치취류수이급린역삼수적정황하，가이자괄응지근거양본간거리관계학정린역반경득도양본밀도，병근거양본밀도축점증가취류중심。위료보장취류결과적정학성，동시제출일충신적모호취류유효성지표이판단최가취류수，소제료밀도취류산법대삼수적민감성。용 UCI 기준수거집진행실험，발현본문산법재대수거진행취류시，취류질량교원시밀도취류산법재준학성화자괄응성방면균유현저제고。
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 cluste-ring algorithm. Experiment results demonstrate that the proposed algorithm improves the clustering accuracy and the adaptability effec-tively.