54 results about "Agglomerative hierarchical clustering" patented technology

A method extracts planes from three-dimensional (3D) points by first partitioning the 3D points into disjoint regions. A graph of nodes and edges is then constructed, wherein the nodes represent the regions and the edges represent neighborhood relationships of the regions. Finally, agglomerative hierarchical clustering is applied to the graph to merge regions belonging to the same plane.

A process planning method based on similarity theory comprises the following steps of: i) completing a part characteristic information model by adding a characteristic combination description on the basis of traditional single characteristic description, and building an description of part characteristics; ii) indicating entities formed by relevant information of relevant characteristic processing procedures regarding the characteristics as core by process elements, and generating a most basic module and a procedure characteristic of process planning; iii) building a case indexing structure based on the characteristics, generating similar process information by the similarity judgment of a part characteristic multi-branched tree, and performing the process similarity judgment; iv) performing procedure combinations by an agglomerative hierarchical clustering method, so as to generate a procedure cluster; and v) sequencing all the process elements in the procedure cluster according to the precedence relation, so as to ensure that all the process elements in the procedure cluster can be processed on a machine tool.

The invention relates to a two-stage hybrid particle swarm optimization clustering method, which is mainly used for solving the problems of greater time consumption and low accuracy of the conventional particle swarm optimization K-mean clustering method when the number of dimensions of samples is higher. The technical scheme disclosed by the invention comprises the following steps: (1) reading a data set and the number K of clusters; (2) taking statistics on information of dimensionality; (3) standardizing the dimensionality; (4) calculating a similarity matrix; (5) generating a candidate initial clustering center; (6) performing particle swarm K-mean partitional clustering; and (7) outputting a particle swarm optimal fitness value and a corresponding data set class cluster partition result. According to the two-stage hybrid particle swarm optimization clustering method disclosed by the invention, the first-stage clustering is firstly performed by adopting agglomerative hierarchical clustering, a simplified particle encoding way is provided, the second-stage clustering is performed on data by particle swarm optimization K-mean clustering, the advantages of hierarchical agglomeration, K-mean and particle swarm optimization methods are integrated, the clustering speed is accelerated, and the global convergence ability and the accuracy of the clustering result of the method are improved.

The invention relates to an enhanced cohesion hierarchical clustering-based distribution network user load feature classifying method, which is characterized by comprising steps: calculating a daily load curve characteristic quantity according to an active power curve and a reactive power curve of users; obtaining a daily load characteristic quantity set (see the specification) of N users, an enhanced damping coefficient gamma and a similar coefficient matrix P(X) among all points; forming all groups of merging routes into a merging route set Sg(s), and calculating a hierarchical clustering cohesion process by using a value iteration algorithm; obtaining a group of routes with a minimal similar coefficient value weight sum in the merging route set Sg(s). By using the clustering enhanced cohesion hierarchical clustering algorithm for the characteristic quantities, return values of all results of each layer of clustering are calculated, the cohesion merging route with the maximal return value is selected, the accuracy of the clustering algorithm is improved, defects such as a sensitive initial value, occurrence of a continuous error, and integral deviation of the result of the hierarchical clustering are avoided, and certain measures are adopted to prevent the influence of a singular value on the results.