CAJ | 학술논문

针对现有直觉模糊集聚类方法存在计算量大、数据失真和易陷于局部最优等问题，提出基于新直觉模糊相似度量的直觉模糊谱聚类算法。首先定义了新的直觉模糊相似度量方法，然后基于该方法构造了直觉模糊相似度矩阵，根据直觉模糊相似度矩阵求解非规范Laplacian矩阵，在此基础上构建特征矩阵，再使用k-means算法对特征矩阵进行聚类。最后在数值算例上的应用证明了所提出算法的可行性和有效性。
침대현유직각모호집취류방법존재계산량대、수거실진화역함우국부최우등문제，제출기우신직각모호상사도량적직각모호보취류산법。수선정의료신적직각모호상사도량방법，연후기우해방법구조료직각모호상사도구진，근거직각모호상사도구진구해비규범Laplacian구진，재차기출상구건특정구진，재사용k-means산법대특정구진진행취류。최후재수치산례상적응용증명료소제출산법적가행성화유효성。
The existing clustering algorithms of inituitionistic fuzzy sets need a large amount of computation efforts, or brings the data distortion, or even may fall into the local optimal solutions. A novel spectral algorithm based on inituitionistic fuzzy sets is proposed. Firstly, a novel inituitionistic fuzzy similarity measure is defined. Next use it to construct an intuitionistic fuzzy similarity measure matrix, by which we present a spectral clustering algorithm to cluster intuitionistic fuzzy information. At last, a numerical examples shows the feasibility and validity of this algorithm.