Title :
A genetic approach to hierarchical clustering of Euclidean graphs
Author_Institution :
Dipt. di Elettronica, Inf. e Sistemistica, Bologna Univ., Italy
Abstract :
We propose an encoding scheme and ad hoc operators for a generic approach to graph clustering. Given a connected graph whose vertices correspond to points within a Euclidean space and a fitness function, a hierarchy of graphs in which each vertex corresponds to a connected subgraph of the graph below is generated. Both the number of clustering levels and the number of clusters on each level are subject to optimization
Keywords :
genetic algorithms; graph theory; image coding; image recognition; pattern clustering; Euclidean graphs; connected graph; encoding scheme; fitness function; genetic approach; graph clustering; hierarchical clustering; Bridges; Buildings; Data mining; Encoding; Genetics; Joining processes; Mobile robots; Prototypes; Robot sensing systems; Tree data structures;
Conference_Titel :
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location :
Brisbane, Qld.
Print_ISBN :
0-8186-8512-3
DOI :
10.1109/ICPR.1998.712002
