Abstract Clustering is fundamental to understand the structure of data. In the past decade the cluster ensemble problem has been introduced, which combines a set of partitions (an ensemble) of the data to obtain a single consensus solution that outperforms all the ensemble members. However, there is disagreement about which are the best ensemble characteristics to obtain a good performance: som...

A new criterion for clusters validation is proposed in the paper and based on the new cluster validation criterion a clustering ensemble framework is proposed. The main idea behind the framework is to extract the most stable clusters in terms of the defined criteria. To combine a set of partitions into one consensus partition, hierarchical clustering algorithms can be employed where first the E...

An online evolving clustering (OEC) method equivalent to ensemble modeling is proposed tackle prognostics problems of learning and the prediction remaining useful life (RUL). During phase, OEC extracts predominant operating modes as multiple clusters (EC). Each EC associated with its own Weibull distribution-inspired degradation (survivability) model that will receive incremental modifications ...

Clustering ensemble generates a consensus clustering result by integrating multiple weak base results. Although it often provides more robust results compared with single methods, still suffers from the robustness problem if does not treat unreliability of carefully. Conventional methods use all data for ensemble, while ignoring noises or outliers on data. some are proposed, which extract data,...