HIERARCHICAL CLUSTERING SCHEMES

Techniques for partitioning objects into optimally homogeneous groups on the basis of empirical measures of similarity among those objects have received increasing attention in several different fields. This paper develops a useful correspondence between any hierarchical system of such clusters, and a particular type of distance measure. The correspondence gives rise to two methods of clustering that are computationally rapid and invariant under monotonic transformations of the data. In an explicitly defined sense, one method forms clusters that are optimally "connected," while the other forms clusters that are optimally "compact."

聚类原始数据集

聚类数据集 %% 利用不同方法对债券样本进行聚类 %说明 %分别采用不同的方法，对数据进行聚类 %可以选择的pdist/clustering距离 % methods = {'euclidean'; 'seuclidean'; 'cityblock'; 'chebychev'; ... % 'mahalanobis'; 'minkowski'; 'cosine'; 'correlation'; ... % 'spearman'; 'hamming'; 'jaccard'}; %Y=pdist(X) 生成各数据点之间距离的行向量 %squareform(Y) 生成方阵（i，j）代表i个点与j各点之间的距离 %聚类方法： %k-means %kidx=kmeans(bonds,numClust,'distance',dist_k); %层次聚类 %hidx=clusterdata(bonds,'maxclust',numClust,'distance',dist_h,'linkage',link); %liankage产生层次聚类树 %获取距离矩阵，第二参数指定距离计算方法