To improve the clustering ensemble effect,this paper designs a unified framework for weighted points,clusters and partitions,and proposes a three-level weighted approach for text clustering ensemble.Firstly,the hyper-graph adjacency matrix is generated according to the base clustering,and then the weighted adjacency matrix is ob-tained by successively weighting the points,clusters and partitions.Finally,the final result is obtained by the hierarchic-al condensation clustering algorithm.Experiments were carried out on multiple real text datasets.The results show that compared with the unweighted results and other level weighted results,this approach has better clustering effect.The av-erage increase of three-layer weighted compared with that unweighted is 12.02%.Compared with the other 8 weighted methods in recent years,the average ranking of this algorithm is the first in all datasets,which verifies the effectiveness of the proposed method.
关键词
文本聚类/聚类集成/加权聚类集成/三层加权/加权聚类/多层加权/聚类分析/无监督学习
Key words
text clustering/clustering ensemble/weighted clustering ensemble/three-level weighting/weighted cluster-ing/multi-level weighting/cluster analysis/unsupervised learning
维普
万方数据