Abstract:

Uncertain tree clustering is an important problem in data mining domain. In this paper, a new uncertain tree clustering algorithm is proposed. The algorithm effectively resolves the clustering problems for uncertain data. In order to improve accurate measurement on the similarities among trees, the method of semantic similarity and structural similarity are presented. A dynamic clustering process is designed in which selfadaptive threshold be applied so as to greatly reduce the jamming impact on the result accuracy. This process can cluster subtrees of similar structure within similar groups , minimizing the similarity of subtree groups. Both simulation and real experiments show that the algorithm is effective and efficient and the clustering result is accurate.

Key words: data mining;ordered tree;frequent subtree;similarity;uncertain tree;clustering