Data Clustering Using Evidence Accumulation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Combining Multiple Weak Clusterings
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Moderate diversity for better cluster ensembles
Information Fusion
Weighted cluster ensembles: Methods and analysis
ACM Transactions on Knowledge Discovery from Data (TKDD)
Unsupervised feature weighting with multi niche crowding genetic algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Knowledge-Based Systems
A comparison of three graph partitioning based methods for consensus clustering
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Multiobjective projection pursuit for semisupervised feature extraction
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
| Hi-index | 0.00 |
Clustering analysis is an important step towards getting insight into new data. Ensemble procedures have been designed in order to obtain improved partitions of a data set. Previous work in domain, mostly empirical, shows that accuracy and a limited diversity are mandatory features for successful ensemble construction. This paper presents a method which integrates unsupervised feature selection with ensemble clustering in order to deliver more accurate partitions. The efficiency of the method is studied on real data sets.