ACM Computing Surveys (CSUR)
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Restrictive clustering and metaclustering for self-organizing document collections
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Positional and confidence voting-based consensus functions for fuzzy cluster ensembles
Fuzzy Sets and Systems
A novel cluster combination algorithm for document clustering
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
| Hi-index | 0.00 |
Consensus clustering is the task of deriving a single labeling by applying a consensus function on a cluster ensemble. This work introduces BordaConsensus, a new consensus function for soft cluster ensembles based on the Borda voting scheme. In contrast to classic, hard consensus functions that operate on labelings, our proposal considers cluster membership information, thus being able to tackle multiclass clustering problems. Initial small scale experiments reveal that, compared to state-of-the-art consensus functions, BordaConsensus constitutes a good performance vs. complexity trade-off.