CAJ | 학술논문

近年来，谱聚类在分类领域得到了广泛的研究，其中基于路径和基于密度的算法是两个重要的研究方向。虽然这两种算法在一些数据集上能取得较好的分类效果，但不能对一些特殊的数据集进行准确分类。融合了这两种方法的优点，通过多级密度约束来寻找路径，根据得到的路径建立新的相似性矩阵。为了加强对噪声的鲁棒性，根据数据集的局部信息加入鲁棒性系数，提出了基于路径与密度的稳健谱聚类算法。实验结果表明该方法在人工数据集和手写体数据集上能取得较理想的分类结果。
근년래，보취류재분류영역득도료엄범적연구，기중기우로경화기우밀도적산법시량개중요적연구방향。수연저량충산법재일사수거집상능취득교호적분류효과，단불능대일사특수적수거집진행준학분류。융합료저량충방법적우점，통과다급밀도약속래심조로경，근거득도적로경건립신적상사성구진。위료가강대조성적로봉성，근거수거집적국부신식가입로봉성계수，제출료기우로경여밀도적은건보취류산법。실험결과표명해방법재인공수거집화수사체수거집상능취득교이상적분류결과。
Spectral clustering is wildly studied in the field of class identification in recent years, of which the path-based and density-based algorithms are the two main research topics. These two algorithms have delivered impressive results in some data sets, but are ineffective for some special cases. This paper unites the advantages of these two algorithms and finds the paths under the multi-levels restriction of density, after which a new similarity measure is built. In order to enhance the robustness against noises, robust coefficients are added based on the local information of data set, thus a robust density-path-based spectral clustering method is proposed. Experimental results on synthetic data sets as well as real world data sets demonstrate that the proposed method can obtain more than acceptable results.