Along with the development of Web2.0, folksonomy has become a hot topic related to data mining, information retrieval and social network. The tag semantic is the key for deep understanding the correlation of objects in folksonomy. This paper proposes two methods to cluster tags for core-tag by fusing multi-similarity measurements. The contributions of this paper include: (1) Proposing the conce...

In modern data mining applications, clustering algorithms are among the most important approaches, because these algorithms group elements in a dataset according to their similarities, and they do not require any class label information. In recent years, various methods for ensemble selection and clustering result combinations have been designed to optimize clustering results. Moreover, conduct...

This paper presents a fast simulated annealing framework for combining multiple clusterings (i.e. clustering ensemble) based on some measures of agreement between partitions, which are originally used to compare two clusterings (the obtained clustering vs. a ground truth clustering) for the evaluation of a clustering algorithm. Though we can follow a greedy strategy to optimize these measures a...

Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochast...

In this paper a new criterion for clusters validation is proposed. This new cluster validation criterion is used to approximate the goodness of a cluster. The clusters which satisfy a threshold of this measure are selected to participate in clustering ensemble. For combining the chosen clusters, a coassociation based consensus function is applied. Since the Evidence Accumulation Clustering meth...

The clustering ensemble technique that integrates multiple results can improve the accuracy and robustness of final clustering. In many algorithms, co-association matrix (CA matrix), which reflects frequency any two samples being partitioned into same cluster, plays an important role. However, generally, CA is highly sparse with low value density, may limit performance algorithm based on it. To...